{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "only use pandas calc nd, please install talib!\n",
      "enable example env will only read RomDataBu/df_kl.h5\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set_context(rc={'figure.figsize': (14, 7) } )\n",
    "figzize_me = figsize =(14, 7)\n",
    "# import warnings; warnings.simplefilter('ignore')\n",
    "import pandas as pd\n",
    "pd.options.display.max_columns = 12\n",
    "\n",
    "import os\n",
    "import sys\n",
    "# 使用insert 0即只使用github，避免交叉使用了pip安装的abupy，导致的版本不一致问题\n",
    "sys.path.insert(0, os.path.abspath('../'))\n",
    "import abupy\n",
    "from abupy import xrange, range\n",
    "\n",
    "# 打开测试数据环境，与书中的例子数据一致，使用RomDataBu下的df_kl.h5数据\n",
    "abupy.env.enable_example_env_ipython()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.6.0 |Anaconda 4.3.1 (x86_64)| (default, Dec 23 2016, 13:19:00) \n",
      "[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)]\n"
     ]
    }
   ],
   "source": [
    "print(sys.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第4章 量化工具-Pandas\n",
    "\n",
    "[abu量化系统github地址](https://github.com/bbfamily/abu) (您的star是我的动力！)\n",
    "\n",
    "[abu量化文档教程ipython notebook](https://github.com/bbfamily/abu/tree/master/abupy_lecture)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 基本操作方法\n",
    "\n",
    "### 4.1.1 DataFrame构建及方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 504)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_day_change = np.load('../gen/stock_day_change.npy')\n",
    "stock_day_change.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>...</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "      <th>500</th>\n",
       "      <th>501</th>\n",
       "      <th>502</th>\n",
       "      <th>503</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.380355</td>\n",
       "      <td>0.122597</td>\n",
       "      <td>-0.285190</td>\n",
       "      <td>-0.008897</td>\n",
       "      <td>0.457319</td>\n",
       "      <td>0.109334</td>\n",
       "      <td>...</td>\n",
       "      <td>1.096122</td>\n",
       "      <td>-0.695501</td>\n",
       "      <td>-1.534180</td>\n",
       "      <td>0.594247</td>\n",
       "      <td>1.246791</td>\n",
       "      <td>0.343928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.133810</td>\n",
       "      <td>-0.493126</td>\n",
       "      <td>1.447011</td>\n",
       "      <td>-1.034918</td>\n",
       "      <td>0.422955</td>\n",
       "      <td>0.366225</td>\n",
       "      <td>...</td>\n",
       "      <td>0.241180</td>\n",
       "      <td>1.061386</td>\n",
       "      <td>-0.817782</td>\n",
       "      <td>1.320760</td>\n",
       "      <td>0.739726</td>\n",
       "      <td>1.703807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.496958</td>\n",
       "      <td>1.174205</td>\n",
       "      <td>0.261256</td>\n",
       "      <td>0.703780</td>\n",
       "      <td>1.318616</td>\n",
       "      <td>-0.479024</td>\n",
       "      <td>...</td>\n",
       "      <td>0.864355</td>\n",
       "      <td>1.212367</td>\n",
       "      <td>0.270861</td>\n",
       "      <td>-0.738852</td>\n",
       "      <td>-0.333497</td>\n",
       "      <td>-0.357874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.570125</td>\n",
       "      <td>0.252668</td>\n",
       "      <td>1.145843</td>\n",
       "      <td>0.293087</td>\n",
       "      <td>-1.299284</td>\n",
       "      <td>0.316227</td>\n",
       "      <td>...</td>\n",
       "      <td>1.366721</td>\n",
       "      <td>-0.295101</td>\n",
       "      <td>-0.957499</td>\n",
       "      <td>-0.251030</td>\n",
       "      <td>-0.141368</td>\n",
       "      <td>0.769644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.307191</td>\n",
       "      <td>0.276648</td>\n",
       "      <td>-0.287054</td>\n",
       "      <td>-0.125589</td>\n",
       "      <td>-0.217242</td>\n",
       "      <td>-0.013351</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.214889</td>\n",
       "      <td>-0.199725</td>\n",
       "      <td>1.099271</td>\n",
       "      <td>0.370935</td>\n",
       "      <td>-0.409567</td>\n",
       "      <td>-0.031080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 504 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0         1         2         3         4         5      ...     \\\n",
       "0  0.380355  0.122597 -0.285190 -0.008897  0.457319  0.109334    ...      \n",
       "1  0.133810 -0.493126  1.447011 -1.034918  0.422955  0.366225    ...      \n",
       "2  1.496958  1.174205  0.261256  0.703780  1.318616 -0.479024    ...      \n",
       "3 -1.570125  0.252668  1.145843  0.293087 -1.299284  0.316227    ...      \n",
       "4  1.307191  0.276648 -0.287054 -0.125589 -0.217242 -0.013351    ...      \n",
       "\n",
       "        498       499       500       501       502       503  \n",
       "0  1.096122 -0.695501 -1.534180  0.594247  1.246791  0.343928  \n",
       "1  0.241180  1.061386 -0.817782  1.320760  0.739726  1.703807  \n",
       "2  0.864355  1.212367  0.270861 -0.738852 -0.333497 -0.357874  \n",
       "3  1.366721 -0.295101 -0.957499 -0.251030 -0.141368  0.769644  \n",
       "4 -1.214889 -0.199725  1.099271  0.370935 -0.409567 -0.031080  \n",
       "\n",
       "[5 rows x 504 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 下面三种写法输出完全相同，输出如表4-1所示\n",
    "pd.DataFrame(stock_day_change).head()\n",
    "pd.DataFrame(stock_day_change).head(5)\n",
    "pd.DataFrame(stock_day_change)[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1.2 索引行列序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>...</th>\n",
       "      <th>498</th>\n",
       "      <th>499</th>\n",
       "      <th>500</th>\n",
       "      <th>501</th>\n",
       "      <th>502</th>\n",
       "      <th>503</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>股票 0</th>\n",
       "      <td>0.380355</td>\n",
       "      <td>0.122597</td>\n",
       "      <td>-0.285190</td>\n",
       "      <td>-0.008897</td>\n",
       "      <td>0.457319</td>\n",
       "      <td>0.109334</td>\n",
       "      <td>...</td>\n",
       "      <td>1.096122</td>\n",
       "      <td>-0.695501</td>\n",
       "      <td>-1.534180</td>\n",
       "      <td>0.594247</td>\n",
       "      <td>1.246791</td>\n",
       "      <td>0.343928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票 1</th>\n",
       "      <td>0.133810</td>\n",
       "      <td>-0.493126</td>\n",
       "      <td>1.447011</td>\n",
       "      <td>-1.034918</td>\n",
       "      <td>0.422955</td>\n",
       "      <td>0.366225</td>\n",
       "      <td>...</td>\n",
       "      <td>0.241180</td>\n",
       "      <td>1.061386</td>\n",
       "      <td>-0.817782</td>\n",
       "      <td>1.320760</td>\n",
       "      <td>0.739726</td>\n",
       "      <td>1.703807</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 504 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5      ...     \\\n",
       "股票 0  0.380355  0.122597 -0.285190 -0.008897  0.457319  0.109334    ...      \n",
       "股票 1  0.133810 -0.493126  1.447011 -1.034918  0.422955  0.366225    ...      \n",
       "\n",
       "           498       499       500       501       502       503  \n",
       "股票 0  1.096122 -0.695501 -1.534180  0.594247  1.246791  0.343928  \n",
       "股票 1  0.241180  1.061386 -0.817782  1.320760  0.739726  1.703807  \n",
       "\n",
       "[2 rows x 504 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 股票0 -> 股票stock_day_change.shape[0]\n",
    "stock_symbols = ['股票 ' + str(x) for x in\n",
    "                 xrange(stock_day_change.shape[0])]\n",
    "# 通过构造直接设置index参数，head(2)就显示两行，表4-2所示\n",
    "pd.DataFrame(stock_day_change, index=stock_symbols).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2017-01-01 00:00:00</th>\n",
       "      <th>2017-01-02 00:00:00</th>\n",
       "      <th>2017-01-03 00:00:00</th>\n",
       "      <th>2017-01-04 00:00:00</th>\n",
       "      <th>2017-01-05 00:00:00</th>\n",
       "      <th>2017-01-06 00:00:00</th>\n",
       "      <th>...</th>\n",
       "      <th>2018-05-14 00:00:00</th>\n",
       "      <th>2018-05-15 00:00:00</th>\n",
       "      <th>2018-05-16 00:00:00</th>\n",
       "      <th>2018-05-17 00:00:00</th>\n",
       "      <th>2018-05-18 00:00:00</th>\n",
       "      <th>2018-05-19 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>股票 0</th>\n",
       "      <td>0.380355</td>\n",
       "      <td>0.122597</td>\n",
       "      <td>-0.285190</td>\n",
       "      <td>-0.008897</td>\n",
       "      <td>0.457319</td>\n",
       "      <td>0.109334</td>\n",
       "      <td>...</td>\n",
       "      <td>1.096122</td>\n",
       "      <td>-0.695501</td>\n",
       "      <td>-1.534180</td>\n",
       "      <td>0.594247</td>\n",
       "      <td>1.246791</td>\n",
       "      <td>0.343928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>股票 1</th>\n",
       "      <td>0.133810</td>\n",
       "      <td>-0.493126</td>\n",
       "      <td>1.447011</td>\n",
       "      <td>-1.034918</td>\n",
       "      <td>0.422955</td>\n",
       "      <td>0.366225</td>\n",
       "      <td>...</td>\n",
       "      <td>0.241180</td>\n",
       "      <td>1.061386</td>\n",
       "      <td>-0.817782</td>\n",
       "      <td>1.320760</td>\n",
       "      <td>0.739726</td>\n",
       "      <td>1.703807</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 504 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      2017-01-01  2017-01-02  2017-01-03  2017-01-04  2017-01-05  2017-01-06  \\\n",
       "股票 0    0.380355    0.122597   -0.285190   -0.008897    0.457319    0.109334   \n",
       "股票 1    0.133810   -0.493126    1.447011   -1.034918    0.422955    0.366225   \n",
       "\n",
       "         ...      2018-05-14  2018-05-15  2018-05-16  2018-05-17  2018-05-18  \\\n",
       "股票 0     ...        1.096122   -0.695501   -1.534180    0.594247    1.246791   \n",
       "股票 1     ...        0.241180    1.061386   -0.817782    1.320760    0.739726   \n",
       "\n",
       "      2018-05-19  \n",
       "股票 0    0.343928  \n",
       "股票 1    1.703807  \n",
       "\n",
       "[2 rows x 504 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从2017-1-1向上时间递进，单位freq='1d'即1天\n",
    "days = pd.date_range('2017-1-1',\n",
    "                     periods=stock_day_change.shape[1], freq='1d')\n",
    "# 股票0 -> 股票stock_day_change.shape[0]\n",
    "stock_symbols = ['股票 ' + str(x) for x in\n",
    "                 xrange(stock_day_change.shape[0])]\n",
    "# 分别设置index和columns\n",
    "df = pd.DataFrame(stock_day_change, index=stock_symbols, columns=days)\n",
    "# 表4-3所示\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1.3 金融时间序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票 0</th>\n",
       "      <th>股票 1</th>\n",
       "      <th>股票 2</th>\n",
       "      <th>股票 3</th>\n",
       "      <th>股票 4</th>\n",
       "      <th>股票 5</th>\n",
       "      <th>...</th>\n",
       "      <th>股票 194</th>\n",
       "      <th>股票 195</th>\n",
       "      <th>股票 196</th>\n",
       "      <th>股票 197</th>\n",
       "      <th>股票 198</th>\n",
       "      <th>股票 199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>0.380355</td>\n",
       "      <td>0.133810</td>\n",
       "      <td>1.496958</td>\n",
       "      <td>-1.570125</td>\n",
       "      <td>1.307191</td>\n",
       "      <td>-0.822087</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.184585</td>\n",
       "      <td>1.245802</td>\n",
       "      <td>0.045201</td>\n",
       "      <td>-0.558521</td>\n",
       "      <td>-1.213311</td>\n",
       "      <td>-0.184731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>0.122597</td>\n",
       "      <td>-0.493126</td>\n",
       "      <td>1.174205</td>\n",
       "      <td>0.252668</td>\n",
       "      <td>0.276648</td>\n",
       "      <td>-1.114597</td>\n",
       "      <td>...</td>\n",
       "      <td>0.457794</td>\n",
       "      <td>0.447693</td>\n",
       "      <td>0.710103</td>\n",
       "      <td>-0.587533</td>\n",
       "      <td>0.093677</td>\n",
       "      <td>-0.463196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>-0.285190</td>\n",
       "      <td>1.447011</td>\n",
       "      <td>0.261256</td>\n",
       "      <td>1.145843</td>\n",
       "      <td>-0.287054</td>\n",
       "      <td>-0.797058</td>\n",
       "      <td>...</td>\n",
       "      <td>1.212614</td>\n",
       "      <td>2.500148</td>\n",
       "      <td>0.814453</td>\n",
       "      <td>-0.130282</td>\n",
       "      <td>-0.310447</td>\n",
       "      <td>-0.946728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>-0.008897</td>\n",
       "      <td>-1.034918</td>\n",
       "      <td>0.703780</td>\n",
       "      <td>0.293087</td>\n",
       "      <td>-0.125589</td>\n",
       "      <td>1.945349</td>\n",
       "      <td>...</td>\n",
       "      <td>0.199341</td>\n",
       "      <td>1.606920</td>\n",
       "      <td>0.084539</td>\n",
       "      <td>0.422849</td>\n",
       "      <td>1.629633</td>\n",
       "      <td>1.155346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>0.457319</td>\n",
       "      <td>0.422955</td>\n",
       "      <td>1.318616</td>\n",
       "      <td>-1.299284</td>\n",
       "      <td>-0.217242</td>\n",
       "      <td>-1.660612</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.815535</td>\n",
       "      <td>0.787376</td>\n",
       "      <td>0.542710</td>\n",
       "      <td>-0.157260</td>\n",
       "      <td>1.019846</td>\n",
       "      <td>0.251412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 200 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                股票 0      股票 1      股票 2      股票 3      股票 4      股票 5  \\\n",
       "2017-01-01  0.380355  0.133810  1.496958 -1.570125  1.307191 -0.822087   \n",
       "2017-01-02  0.122597 -0.493126  1.174205  0.252668  0.276648 -1.114597   \n",
       "2017-01-03 -0.285190  1.447011  0.261256  1.145843 -0.287054 -0.797058   \n",
       "2017-01-04 -0.008897 -1.034918  0.703780  0.293087 -0.125589  1.945349   \n",
       "2017-01-05  0.457319  0.422955  1.318616 -1.299284 -0.217242 -1.660612   \n",
       "\n",
       "              ...       股票 194    股票 195    股票 196    股票 197    股票 198  \\\n",
       "2017-01-01    ...    -0.184585  1.245802  0.045201 -0.558521 -1.213311   \n",
       "2017-01-02    ...     0.457794  0.447693  0.710103 -0.587533  0.093677   \n",
       "2017-01-03    ...     1.212614  2.500148  0.814453 -0.130282 -0.310447   \n",
       "2017-01-04    ...     0.199341  1.606920  0.084539  0.422849  1.629633   \n",
       "2017-01-05    ...    -0.815535  0.787376  0.542710 -0.157260  1.019846   \n",
       "\n",
       "              股票 199  \n",
       "2017-01-01 -0.184731  \n",
       "2017-01-02 -0.463196  \n",
       "2017-01-03 -0.946728  \n",
       "2017-01-04  1.155346  \n",
       "2017-01-05  0.251412  \n",
       "\n",
       "[5 rows x 200 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df做个转置\n",
    "df = df.T\n",
    "# 表4-4所示\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票 0</th>\n",
       "      <th>股票 1</th>\n",
       "      <th>股票 2</th>\n",
       "      <th>股票 3</th>\n",
       "      <th>股票 4</th>\n",
       "      <th>股票 5</th>\n",
       "      <th>...</th>\n",
       "      <th>股票 194</th>\n",
       "      <th>股票 195</th>\n",
       "      <th>股票 196</th>\n",
       "      <th>股票 197</th>\n",
       "      <th>股票 198</th>\n",
       "      <th>股票 199</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>0.101295</td>\n",
       "      <td>0.367820</td>\n",
       "      <td>0.075089</td>\n",
       "      <td>-0.102398</td>\n",
       "      <td>0.141279</td>\n",
       "      <td>-0.144679</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.145753</td>\n",
       "      <td>0.196853</td>\n",
       "      <td>-0.296633</td>\n",
       "      <td>-0.455264</td>\n",
       "      <td>-0.056664</td>\n",
       "      <td>0.183957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-22</th>\n",
       "      <td>-0.058951</td>\n",
       "      <td>-0.377245</td>\n",
       "      <td>0.376972</td>\n",
       "      <td>-0.011026</td>\n",
       "      <td>0.381368</td>\n",
       "      <td>0.016134</td>\n",
       "      <td>...</td>\n",
       "      <td>0.047927</td>\n",
       "      <td>-0.301956</td>\n",
       "      <td>0.201439</td>\n",
       "      <td>0.145753</td>\n",
       "      <td>0.196472</td>\n",
       "      <td>-0.035589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-12</th>\n",
       "      <td>-0.138417</td>\n",
       "      <td>-0.244038</td>\n",
       "      <td>0.001510</td>\n",
       "      <td>0.036972</td>\n",
       "      <td>-0.183559</td>\n",
       "      <td>0.355932</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100974</td>\n",
       "      <td>-0.097821</td>\n",
       "      <td>0.208232</td>\n",
       "      <td>0.053988</td>\n",
       "      <td>0.006973</td>\n",
       "      <td>0.077153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-05</th>\n",
       "      <td>0.385774</td>\n",
       "      <td>-0.102745</td>\n",
       "      <td>0.245720</td>\n",
       "      <td>-0.219062</td>\n",
       "      <td>0.085310</td>\n",
       "      <td>-0.135895</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225311</td>\n",
       "      <td>0.257156</td>\n",
       "      <td>0.140883</td>\n",
       "      <td>0.371555</td>\n",
       "      <td>-0.193469</td>\n",
       "      <td>-0.265355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-26</th>\n",
       "      <td>-0.076464</td>\n",
       "      <td>-0.008265</td>\n",
       "      <td>0.038223</td>\n",
       "      <td>0.118823</td>\n",
       "      <td>0.647744</td>\n",
       "      <td>-0.257020</td>\n",
       "      <td>...</td>\n",
       "      <td>0.113628</td>\n",
       "      <td>-0.461755</td>\n",
       "      <td>0.031637</td>\n",
       "      <td>-0.130856</td>\n",
       "      <td>-0.258763</td>\n",
       "      <td>-0.113054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 200 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                股票 0      股票 1      股票 2      股票 3      股票 4      股票 5  \\\n",
       "2017-01-01  0.101295  0.367820  0.075089 -0.102398  0.141279 -0.144679   \n",
       "2017-01-22 -0.058951 -0.377245  0.376972 -0.011026  0.381368  0.016134   \n",
       "2017-02-12 -0.138417 -0.244038  0.001510  0.036972 -0.183559  0.355932   \n",
       "2017-03-05  0.385774 -0.102745  0.245720 -0.219062  0.085310 -0.135895   \n",
       "2017-03-26 -0.076464 -0.008265  0.038223  0.118823  0.647744 -0.257020   \n",
       "\n",
       "              ...       股票 194    股票 195    股票 196    股票 197    股票 198  \\\n",
       "2017-01-01    ...    -0.145753  0.196853 -0.296633 -0.455264 -0.056664   \n",
       "2017-01-22    ...     0.047927 -0.301956  0.201439  0.145753  0.196472   \n",
       "2017-02-12    ...     0.100974 -0.097821  0.208232  0.053988  0.006973   \n",
       "2017-03-05    ...    -0.225311  0.257156  0.140883  0.371555 -0.193469   \n",
       "2017-03-26    ...     0.113628 -0.461755  0.031637 -0.130856 -0.258763   \n",
       "\n",
       "              股票 199  \n",
       "2017-01-01  0.183957  \n",
       "2017-01-22 -0.035589  \n",
       "2017-02-12  0.077153  \n",
       "2017-03-05 -0.265355  \n",
       "2017-03-26 -0.113054  \n",
       "\n",
       "[5 rows x 200 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_20 = df.resample('21D', how='mean')\n",
    "from abupy import pd_resample\n",
    "df_20 = pd_resample(df, '21D', how='mean')\n",
    "# 表4-5所示\n",
    "df_20.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1.4 Series构建及方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2017-01-01    0.380355\n",
       "2017-01-02    0.122597\n",
       "2017-01-03   -0.285190\n",
       "2017-01-04   -0.008897\n",
       "2017-01-05    0.457319\n",
       "Freq: D, Name: 股票 0, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stock0 = df['股票 0']\n",
    "# 打印df_stock0类型\n",
    "print(type(df_stock0))\n",
    "# 打印出Series的前5行数据, 与DataFrame一致\n",
    "df_stock0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1159b32b0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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evqsP77xvUJ3CVy+zPMZaGevssW+9zKxTXbodRDKdxwMnenCf36fe7rBKgy6W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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11597e550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_stock0.cumsum().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1.5 重采样数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>0.380355</td>\n",
       "      <td>0.666184</td>\n",
       "      <td>0.208865</td>\n",
       "      <td>0.666184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>0.775518</td>\n",
       "      <td>3.543428</td>\n",
       "      <td>0.407425</td>\n",
       "      <td>3.543428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>4.897457</td>\n",
       "      <td>4.897457</td>\n",
       "      <td>2.917848</td>\n",
       "      <td>3.350685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-16</th>\n",
       "      <td>2.802112</td>\n",
       "      <td>2.905378</td>\n",
       "      <td>1.329033</td>\n",
       "      <td>1.329033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-21</th>\n",
       "      <td>2.127188</td>\n",
       "      <td>3.107789</td>\n",
       "      <td>1.262423</td>\n",
       "      <td>3.107789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                open      high       low     close\n",
       "2017-01-01  0.380355  0.666184  0.208865  0.666184\n",
       "2017-01-06  0.775518  3.543428  0.407425  3.543428\n",
       "2017-01-11  4.897457  4.897457  2.917848  3.350685\n",
       "2017-01-16  2.802112  2.905378  1.329033  1.329033\n",
       "2017-01-21  2.127188  3.107789  1.262423  3.107789"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from abupy import pd_resample\n",
    "# 以5天为周期重采样（周k）\n",
    "# df_stock0_5 = df_stock0.cumsum().resample('5D', how='ohlc')\n",
    "df_stock0_5 = pd_resample(df_stock0.cumsum(), '5D', how='ohlc')\n",
    "# 以21天为周期重采样（月k），\n",
    "# df_stock0_20 = df_stock0.cumsum().resample('21D', how='ohlc')\n",
    "df_stock0_20 = pd_resample(df_stock0.cumsum(), '21D', how='ohlc')\n",
    "# 打印5天重采样，如下输出2017-01-01, 2017-01-06, 2017-01-11, 表4-6所示\n",
    "df_stock0_5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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dgyE7ewIADI4ACQC4NZ3Ap657crpug6FO0+vLD7y5J7cLAMCNWcI2ZhZnl3LiyMmh3xYd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1114dfc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from abupy import ABuMarketDrawing \n",
    "# 图4-2所示\n",
    "ABuMarketDrawing.plot_candle_stick(df_stock0_5.index,\n",
    "                                   df_stock0_5['open'].values,\n",
    "                                   df_stock0_5['high'].values,\n",
    "                                   df_stock0_5['low'].values,\n",
    "                                   df_stock0_5['close'].values,\n",
    "                                   np.random.random(len(df_stock0_5)),\n",
    "                                   None, 'stock', day_sum=False,\n",
    "                                   html_bk=False, save=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "DatetimeIndex(['2017-01-01', '2017-01-06', '2017-01-11', '2017-01-16',\n",
      "               '2017-01-21', '2017-01-26', '2017-01-31', '2017-02-05',\n",
      "               '2017-02-10', '2017-02-15',\n",
      "               ...\n",
      "               '2018-04-01', '2018-04-06', '2018-04-11', '2018-04-16',\n",
      "               '2018-04-21', '2018-04-26', '2018-05-01', '2018-05-06',\n",
      "               '2018-05-11', '2018-05-16'],\n",
      "              dtype='datetime64[ns]', length=101, freq='5D')\n",
      "Index(['open', 'high', 'low', 'close'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(type(df_stock0_5['open'].values))\n",
    "print(df_stock0_5['open'].index)\n",
    "print(df_stock0_5.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 基本数据分析示例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from abupy import ABuSymbolPd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-07-20</th>\n",
       "      <td>228.36</td>\n",
       "      <td>229.800</td>\n",
       "      <td>225.00</td>\n",
       "      <td>1.38</td>\n",
       "      <td>226.47</td>\n",
       "      <td>225.26</td>\n",
       "      <td>2568498</td>\n",
       "      <td>20160720</td>\n",
       "      <td>2</td>\n",
       "      <td>499</td>\n",
       "      <td>9.192273</td>\n",
       "      <td>8.723406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-21</th>\n",
       "      <td>220.50</td>\n",
       "      <td>227.847</td>\n",
       "      <td>219.10</td>\n",
       "      <td>-3.44</td>\n",
       "      <td>226.00</td>\n",
       "      <td>228.36</td>\n",
       "      <td>4428651</td>\n",
       "      <td>20160721</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "      <td>9.171070</td>\n",
       "      <td>8.725091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-22</th>\n",
       "      <td>222.27</td>\n",
       "      <td>224.500</td>\n",
       "      <td>218.88</td>\n",
       "      <td>0.80</td>\n",
       "      <td>221.99</td>\n",
       "      <td>220.50</td>\n",
       "      <td>2579692</td>\n",
       "      <td>20160722</td>\n",
       "      <td>4</td>\n",
       "      <td>501</td>\n",
       "      <td>9.185781</td>\n",
       "      <td>8.779013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-25</th>\n",
       "      <td>230.01</td>\n",
       "      <td>231.390</td>\n",
       "      <td>221.37</td>\n",
       "      <td>3.48</td>\n",
       "      <td>222.27</td>\n",
       "      <td>222.27</td>\n",
       "      <td>4490683</td>\n",
       "      <td>20160725</td>\n",
       "      <td>0</td>\n",
       "      <td>502</td>\n",
       "      <td>9.266934</td>\n",
       "      <td>8.929798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-26</th>\n",
       "      <td>225.93</td>\n",
       "      <td>228.740</td>\n",
       "      <td>225.63</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>227.34</td>\n",
       "      <td>230.01</td>\n",
       "      <td>41833</td>\n",
       "      <td>20160726</td>\n",
       "      <td>1</td>\n",
       "      <td>503</td>\n",
       "      <td>9.133747</td>\n",
       "      <td>8.754098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close     high     low  p_change    open  pre_close   volume  \\\n",
       "2016-07-20  228.36  229.800  225.00      1.38  226.47     225.26  2568498   \n",
       "2016-07-21  220.50  227.847  219.10     -3.44  226.00     228.36  4428651   \n",
       "2016-07-22  222.27  224.500  218.88      0.80  221.99     220.50  2579692   \n",
       "2016-07-25  230.01  231.390  221.37      3.48  222.27     222.27  4490683   \n",
       "2016-07-26  225.93  228.740  225.63     -1.77  227.34     230.01    41833   \n",
       "\n",
       "                date  date_week  key     atr21     atr14  \n",
       "2016-07-20  20160720          2  499  9.192273  8.723406  \n",
       "2016-07-21  20160721          3  500  9.171070  8.725091  \n",
       "2016-07-22  20160722          4  501  9.185781  8.779013  \n",
       "2016-07-25  20160725          0  502  9.266934  8.929798  \n",
       "2016-07-26  20160726          1  503  9.133747  8.754098  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# n_folds=2两年\n",
    "tsla_df = ABuSymbolPd.make_kl_df('usTSLA', n_folds=2)\n",
    "# 表4-7所示\n",
    "tsla_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2.1 数据整体分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JQhK/+xrOPBOiouCqq2rfdThFEdch8qSkxMBRfYI0ruNZ+n1g0nPQqT1kZUFm\nJrylKz6a2p5V+Jr17FpozNh+54bfT1JioGdPTCtXkmLwQWJioX1OZSL9PSgtMoPD4WgKfAu87XQ6\nP3c4HPFOpzMjsPpbYDKwGCh4JjFABqWQnl723gAhVJXkXbvwd+oCqoJx6xaOtT8bUiOfYlVuzuik\n/1NVkqJjUFeuIr2I87Dv2EMUkG6JwV/EerNqJA5wHUolL5Kfg9dL8uHDKG0c5Dw9Ee+ZnQtdF6M9\ngYTAa+WVVzn+X13lTEpNJeHCvhgO7Acgq8lpeAZfWZPWl0pKSgypdfHv7CShTn7+qkrS7G8hLh5/\n5y5ILhemtavxjryRzFnfY9y0gbjhVwGaHi2IjUOLicG4eRNSlJ1jLc8CnwGuvRmuvJa4kcMxL9Tr\nPvjsMzSDgbSLrkAr7XMxRoMbcFfu86uN1yBKNmMH0vcdxd+4eNuiv/8JG5DerZd+H9UspADeg4fJ\nLOac5COHSXrxRbSoKHzde2LYvQvDrl24xk/E/MfvZH76VVidY9QL/8P+6ssoDRshZ2aQ8+Cj5CU2\nqtzvU++BJCUmIr3xRn4q3fTpuOf+iPu6kfj6DaiVEz+nArXx+3CqEbwGcf/uwAyk2pP075s1Hm66\nK7Rdwh+LMP6zkdyoWHJOuGZScmOSA681oxEtIZG0hIaFvrdR5w3Avnw5WbO/r3XPJ5Gkpr4HJTlM\nJdbMOByO+sA8YLTT6Qy2mP/V4XB0D7w+H1gHrAb6OBwOq8PhiAPOBDZV1vCikI4dQ/L7URs0JOu9\nj0j/fp4epanLyDL+Tp0xbvuX+Av6Yv3kQz3qFFydegQAtV79InevLTUz8qGDSJqGv0MnvAMvKnIb\n/5ltcV+ph3LlY6n6TKfXS+wtIzAc2I/7iqEA2J99Rp99FgjqMAbnVuTsLLwXDyLzqzlk/Pgbnksu\nw7xsCebffsU+8WnkY6mocfHgdmPcvg3zsiXI6el4Bl0GZnP+YFYrmTNnk/b3Vn17wNenb40JmtRa\nCggAFIumYV64ADUuXp8IAzCbUWPjShQAMG78G4Dcex8gc+4voeL96AnjMC9ZhO2D6fnbrltD1Ouv\noDRtRvqSVRzbfZi8+x+u1KkBEBVFzhPjw2uCnnsO69xviR82lIS+PbHM/rryxxEI6iqahmH7v7rk\neREiSgCZM2eRN/Jm8u6+r/Du0TH4OnbG38ZB2r97OL5wBRgKZw35euhN1o2bNlat/YJKU5oAwBNA\nAjDO4XCOM3NaAAAgAElEQVQsdDgcC4GHgNcCr89FVy47DLwJLAEWAGOdTme1aO8ajugpTErDhqhN\nm6G071Adh6lxcsaOx3P+BRg3bSDm4VEkdTwD+/ix4PEgHwk4M8X0JqgtzkwwqqKUlBtuNpP97vu4\nrx6GlJuLvHsX0ePGYF65HM/lQ8h+70Nyb70Dw+5dWD9+v/hxBII6gGnNKgB8wVo4SSLnkTEAxN5x\nM+aFC/D26Uf6ij85vsHJsd2HSD14nGPb9pI9ZVrhAWUZtWEjPJcPBsATcP5PZdSAOpt89Eix2xh2\n7cCwb6/eW6zAQ4qalFSiNLNxg+7M+Dt01Ldv0DBsfdSUN3SZ+dxcYu69AzSN7MnvVrlinPu6kbiv\n+k/+gp07UVqchvuq/2DYvo3YO2/BIB6wBKcoxr/WYTh4AG8JSoFq/Qa4Xn690Hc4SMaPv5E+fyla\ndEyhWt8gIcnnAqpogtpBaTUz9wP3F7GqkKyW0+l8D3iviuwqFvmw7syo9Yv+g6yr+LueTdYXs5AP\nHcT68QdYP/mQqHcmo9arr/9IJyWFz9IWINQ0M8J9ZuRgd+vGRQsVFMR/VjvgS6KfHovll5/wn9mW\nrDfeBkki96HRWGd+jv2VSbiHX1/3I2+CUwtNI+7qwXrDR6N+i/UVEPZQ2nfA36o1xkAvppwnx4fv\nbzSixcVTErmPPYHatBnua4ZXre11EN85+s+R+Y/f8QQaTZ6I6Q89Nc/bb0DYci0pWS8c1rQiU7Xy\nnZlOACgNGoStl1OPYl66CNOC3zHu2E7unffi69W7cidUFAYD2e9MRzm9FfYXnwPA16kz2e9Mx9u3\nP7Gj7sK8fAl57dpX/bEFglqOZc5sIF8ltkIU83xVELVefdSY2EKSz4LIU+eaZsqB4nL1hB+VkwW1\nYSNyR48lfckqNJsN2wfvIR8+rBfWF0MoMuOKbJ+ZYGRGbVIWZ0bvH2H55Se9/8JHn4ecFi05GffN\ntyGnpWFeurikYQSCWod8+BDmJQuxzv4ay6yvUBMSUBxnhG2Td+8DaJJE9mtv4e9cuPi0NNQGDcl9\n8NFi+8+cSihnnInSpCnmBfPB5ytyG/PC+QB4+/YPW64mJyP5/UiZRZd4Gjf+jZpSD7W+/ntTcBJN\nCSiaRb30PFHTp+Jv4yDniacqezolojRrnv+6hS4g4evZS7d1zepqPbZAUCtRVSxzv0WNiy80WVHl\nFJB8xu+v3mMJykXdc2aCkZliQoUnC1pCIu6rh2HYu0eXby7Bect3ZqomMmP58nOSHM3z5ZPz8jAt\n+B37uMeJHzQQc6CJ1InI+wNpZo1LlyD1t9VnEDVZJmvah4UaaHr7nw+AadmSip6GQBARjOv/yn9j\nserpYid0fHf/93rSnLtxXzeyhq07CZEkvBdchJyZQdQbrxRqHhz98Cgsv/6Mv40DtXmLsHUlyTNL\nWZkY9u3F3659KGqjFphU8gy5CjUuHtNff6IZjfp1tlqr+OTCUQs6M6fpzozavAVqSj1Mq1fqESaB\n4FRi+XIMBw/oNYY1MLmjtGqD5PUi79X72ojvXO2g7jkzwZ4rgX4lJzN5t9yR/6akyEywz0IV1czE\n3ncncno61o9mYPz7L5LatSZ++JVETZ2Cae1qbO9MBvSuuca/1oHXi3X6u1i+/xZNllEbl9KDAdDq\n1yfnocfInjJNbyR3Ar6uZ6NZLJiWL62ScxIIagrj338CkP3KmxxfvrZoMQxJQotPKLxcUCHc141E\ns9mwv/gciR3OIPrxR5Ay0pEPHcT2yYf4W7ch6/3CjSe1gDMjpRbhzASalAajMhA+iaa0OA1vf30m\nOPehx/B37Fyl51QUStNm+bYEnBkkCd/ZPTAcOhhK9RUIThm+/BKgxtTFgnUzthlTiR35X5JbNCDq\n+Qk1cmxB8ZQqzVzbCEYLTtY0s4IoZ7XF26s35uVLS4zMIMuo0TFV5swoLU7DsHsXxs3/YNi3Dzk7\ni7ybbsUz6HLszz6NadUKpONpxF17jb59s+YY9u5BjY7B9fzLaDGxZTpO7pgni19pteLr0g3TyuVI\nGeniwU9QZzAFIjOeQZejJZ3iSmM1hL9DJ9LWbsL26YdYP5yBbcY0cLvx9T4PAPd/R6C0KdzrRa2n\ni6rIqUcLrQs2NFZjYwtsn68oqTZpQs4T4/Gf3YO8G2+t0vMpDrVBQzSjEcnvD0VmAPwdO2H56XuM\nzi14Czg8gupB3r8PLSoq8n3dTnUUBb7+GjUpSRf3qAH8AWcm6r13Q8vMy5ZSgUYjgiqk7kVmDh9G\ni7KHit5PdkIygu3albidFh2N7Ko6ZwbAtG4t5p9+QGnWHNcLr+Dr2x/vhZcgKQrWr2eGtpcPHST3\n9rs4vvpv3DdV3Y+6r1dvJE3DtGpllY0pEFQnUnYWxr/WoTRrIRyZGkZLSSH3wUc5vnYjanIK5t9+\nDUV2fecWXZQfdE6KUkKTsnRnJmxyxmxGTdaVjpTGTVFbnEbebXeByVSVp1I8BoMenbHbwxwrJSC6\nIh84UDN2nMLIhw+R1KUtcTdcG2lTTnlMy5fCkSN4Lh1cY99B74ALyLt2BDmPj+P4srUojRojHxTf\nu0hTd5wZv5+YO2/BtGmDrihzijQJ8154CWlrN8J1Rav0BNFiYpCqyJnB4wH02Uo5x4X7ymtCn7f3\nAj1lxvrFZ/qmlw/h+NqN5EychJacXPR4FcR3bh9A1M0I6gbSkSPEDR6k94i5ZFCkzTl1MZnw9j8f\nw9Ej2D75EDU6Bn/7jkVuGnJmUktwZk6QWVYCqWZlETqpDlwvvgaffhr2GxhUkJQPiYeq6ibqhYkA\nmFat0LvLC6oM0x/zscz8rMzbW+bMAsAzpAYbWNrtuF6fQu6Dj6K0boPaqLFey60oNWeDoBB1xpmx\nTX8X6+yv0YzG0AP1qYLarHmRDZwKosVUXZpZQSEBNTkF9w03h977z2yLJssYN+s9UX3duldb/ZKv\n69loZrOomxHUbtxuDDu2kXDpBZg2bSBvxE3kjJ8YaatOabznXxB67et9Xkgi+0TyIzOF08ykrEwA\ntNjwtFn3jbeQd/NtZU6nrWp8ffvDkCFhy5TAPdhwikRmjGtXY9ywvsaPG/XCRGyff5K/YImYaKsq\npKNHib1lJLGj7grVq5WI34/lx7nQoEFInj0SKI0aI/n9ehNwQcSoE86MfPAAUZOeQ01MJG3TNnL+\n90KkTap1aDGxSB4P5JTQBbuMSK5s1Lh4Mr6ZS9r6LeF9Y8zmsPfVWrtks+Hr0g3jxr+LlU4VCCKJ\nYesWkk9vTOI5XTHs3U3Oo4/jevn1Yh+eBTWDt//5KA0a4u3TF9dLrxW7Xahmpog0s6JqZgDcI2/C\n9cIrVWht5VEDMtGnRLqLphE3cjixN11fo4eVD+zH/uqLKM1akP1i4G9qwYIateFkxv78hFCqvGlZ\n6S0Z5L17kI8fh4EDS53srU5C371AawpBZKgTzoz9qSeQc1zkjJuAlijy0IsimEZhWl22+hLrB9Mx\nLVmE+fvvsD/xKOYfvw+tk10uvaDuvH5FNpJSCsgoV7eqXKhuZuWKaj2OQFARTMuWIAV6m2S98Ta5\njz5+yqTA1ma0hESO/7WZzFnfh6mRFdouJhbNai17zUxtxWpFTU4+JR6opNRU5GPHMOzbi3Sk8HWr\nLgw7dwDgvvo/uIddi2axCGemijBuWI/1809QExMBMC9aWOo+ht279BetW1ejZaWjNtKfgeSDByNq\nR1FYvp5J9P13g6pG2pRqp9Y7M6YFv2Od+y2+bt1x/7dmZ2LqEt6Aao95yaJSt5WOHCFm9ENEP/Yg\nMaPuImr6VGLvuiVUKyPl5JQosKAU6NWgVHO/n1DdjEg1E9RCjM4tAKT/vhiPuD/VLsoyWytJqPXq\nF5NmFqyZqQPODKA0aoLh4IGTvu+FsUD3dVNABr282MePJeq5CeX6rAx7dgOB3z+bDd/ZPWD9eqTj\nZUiJEhSPphE9djSSppH19nTUmFjMSxaWulvImTn99Oq1rxSC4huGg7VsIkFVib3ndmxffBpyxE9m\narcz4/EQM+ZhNIOB7JdeL9R4TpCPr8c5aCYTpqWlOzOmdWsAMO7Yjpyj18dIbjfGv9eDqiLl5qBF\nRxe7f0FnpqRZz6pA1M0IaiWahmHLZgzOrWiShL9Vm0hbJKggako9XZr5hNnLYPPNEwUAaitqo8ZI\nbjdS+vFIm1KtGLb9G3od1qC2rGgaUe9Mxv76y9gnPl3m3YJNEoONS4Oy36Zl4repMljmfotp1Qo8\nF1+Kb8BAfN17YNi9q9TU8trizASzU2pbZMYYeM4DMGz5J4KW1Ay12jswrVuDYfcu3NeOQGlbsjTx\nKY/djq/r2Rj/Xo+UkV7ipqYCf+QA7iuG6stXLkcKODclOTNqIM1MTUgAm60yVpdOVJSomxFEDOv7\n7xE16dkilk8jsW9PzCuW6V3lo6Jq3jhBlaDWq4/k8xW6b8qZugCAWlecmcbB3P0I1M34/XrK8rjH\nq6RusyQM2/MjM/aXXyD+4v4QSPUsC0FhBwDbu2/pkZXcXOzPjCP22quhgABO2HH37gb0vmoA3t56\nXxNzGSYQBcWgadgnPo1mMuF6WhdNCdWgFBEtLUitcWZC9Wq1KzJjmftt6LVxy+YIWlIz1GpnJljM\nWJyspiAcX5++en1JKTNFxhOcmdwHHwXAtGp5SMmsLJEZtUH11ssE8fU6F0lViRs2NGxWTiCoLqIf\nvp+YO28mZszD2F+ZFP6ApihEvfNW6K3/jDMjYKGgqihO0aw4NbPaitK4KQCGQAShprB+MJ3k5vWJ\nu2UEUVOnYHv/vSod37BxAwl9exJ78whMy5Zg2K7/BiiB62b6cx2WObOQsrMwLfgdy+yvS0wfK6iU\nJfl82Cc+TWK/c4ia8gaW3+dh/ebLou3YswfNaAw9vPo7dwG7HdPS0ovVBUUjpaZi2LMb74CBqC11\np0RNKV6UoyCGPbtQo2OgiltClBe1fgM0gwFDLYvMmNauDr0WzkyECYbtggVWgpLx9ukHUGK+qXQ8\nDdP6P/G3bqMX+XfugtK2HUrzFphWr8rPE7eXUDPT4jQ0oxGlefOqNL9YPNcMx9epM6Y/1xH9xKM1\nckzBqYuUlobtkw+wzv4mtMy4Pd+Jtsz9NuyBUbNYa9Q+QdUSUjQ7QQlMys5CM5vBWjeur/+stgAY\nN/5do8c1L1mE5POF6jZt770DXm+VjG1avpT4IYMwbtmM5YfviB96KZbf56Emp5D5zVyypkxDMxiI\nefQBklo3I374lcTeeQumxQuLHVMO1Li4rx6GJsvYPv0Iee8eXW7baMT2wXSsn31MQp/u2MeNwfCP\n3obAsGc3apOm+bVYJhOcdx7Gbf/qfUYE5SZYc+g/s21oWciZSQ2fXJB37cT+7DPY3nwVKS0Nw57d\nuhhRpAVXDAbU+g1qnZKgfPQoSuMmqPHxGLbWbWdGSj+OYdPGEreptc6MYcc2DIEGYNWtmHWy4O/S\nFS0qKjRTZFq4gKTWzUKRGPnQQeIHX4KUm4tn6NWk/zSfrI++AMDXsxdyZgbGP9cCJUdmtPgEMr+a\ng6uGJLKV01uTMW8R3vP6Y170h5gJE1QrppXLCy0zbNV/dKUjR4h+4lE0s5mst99DadhI7wAvqLPk\nK0GGKyZKWVl1JioD4O/YGQDT+ooVxVeUYGpy5qdfkXvHPRgOH9KjI5VE3rWTuGFDkdx5ZE37gPTv\n5+G+8mo0oxHfOeeinHEmnmuG477+RlBVfD3OwT30KgBMy4vv/yIHIjP+s9rhvuFmfF3PJuOn33G9\n8AqeQZdj3PIPMQ/ei9G5laipb5PYvxfxA89DPpaK0qxF+GADBujHE79JFcIQcGYUxxmhZfmR0kBk\nxuUi6rkJJPbpTtQbrxA98WliHroPKTc3lPIeaWpd40xNQ049ilqvHv4z22LYtRPy8iJtVYWJufs2\nEi7qV+I2tdKZMf/8I4nndMU2YxoASsPGEbaojmA24+vZC+O/TuTDh4h6/WXkzAysn3+CvHMH8Zdd\niNG5ldw77iH3ocdQT2uJGlAj8/U4BwDL7/OAkp0Z0Isf1QJCADVBzpixaJJE7A3XVl/DNI+nTn/p\nBZXHtEJP08z47mcyvvsZAKNzq64Oc98dyGlp5Dw1Ac/Vwzj+91b83XtE0lxBJfGd2xvNYMC86I+w\n5VJWFmpdkGUOoCUloTRrjvHvv8qm0qUomJYvxVQGBcySkHJz0SQJbDby7rgbzWAg6u03K62qZvpz\nLZLHQ86YcXiGXIW/R0+y332ftH/3kPXO9NB2rpde49ieI2R+9zOul15Hk2VMKwpPSITsDURm1KQk\nXJNeJePn+fi7dAuNlXvvA7iHX8fxpWvI/OAzPBddgvEffVZYadUqfDDhzFQK49atAPgd+am6+Wlm\nR7HM/prEc7thf/1l1OQUst6aihofj+XnH4B8FddIE2qcmVpynU9NIWVnIbndqCn1UFq1RlLVkBpf\nbcf21huYf/4x9N6wczuW+b+FWiAUR4md3RwOhwl4H2gBWICJwF5gMqAAHmCk0+k84nA43gB6A8E2\n9IOdTmdmoUHLgOWn/J4nmtmMliR6y5QVb59+mBf8jm36VMwBBTDbJx9i+el7/SHs8XHkPvBIodCs\nr2cvQI/mACVKM0cKf7fuZE9+l9h778D29ptkv/t+lR8j9taRmJcsxjXuGdw33SoU9E5BTCuWo1ks\n+Dp3RQrUyhicW7BNexvzwgV4BwwU0ZiTCC0mFn/XszGuXY2UkY4WnwDoTTP9dSwrwNepC9a53yLv\n2xtS3ToRg3Mr1o9mYJk7B8PRI2gGA2mbd6AlJFbsoDk5aPZoXea6SVM8Q67COusrzAt+w3v+hRU+\nl2ANk3J6uANR5G9T4PdMi43D364Dpj/XQm4u5iWL8PbtH5YqKB87pm9bxHOFlpBIzlMTQu+VNg68\nl16OdPQo5j9+x9vv/PAdOnZEjY/HLJyZCmF0bkGTZZRW+b1igmmfUW++CoBmsZDz0GPk3vegXqO0\nbAm2Lz5FM5nwDL2K2vCkUrBprVrN7SrKQtCpUlPqheyRjxxGqeX1nYbt24ieMA6A1KN6yYP1w7I9\n55X2pHY9kOZ0OvsAFwNvAW8A9zmdzn7AbGB0YNuuwEVOp7Nf4F+FHBk0LfRADYEi80jnRNYhfH0C\nectvvQ7kF+vLaWlkP/+SXuxfxOepnN5Kb7oWlCMtJTITKTzXDEdNSsK0elWVjy3v3IHl15+RcnOI\nefwR4oZeinwK6LML8pEyMzBu2oCvSzewWtGSklCTU7D89iv2iU/rs4NvvivuSScZ3r79kVQV09JA\nepLPh5SXV6fSzCA/1cz4d/GSxbEjhxM1fSqSz4vStBmSooQe8CuClONCs9tD73PvuR8A25Q3Kzwm\n5KcZBR9uy4rvnF5IXi/xl19E3Ihh2KZOCR83GJkpRwNurV49PMOuRatfP3yFwYCvVx8Me/cg15GZ\n71qDpmFwbkE5rWWYs6kVuN6aJJG+aAW5Y56EwN+Y94oh+v8XXlJxB7yKCTXOjISSYBEEJwLUegWc\nmdpc16Wq2N57h+gnR+cv0zTIzcX6xae6U1ZKUKM0Z+ZrYFzgtQT4geFOpzOY42ME3A6HQwZaA9Mc\nDscyh8Nxc/nPBlAUzPN+wXDkcGiReuLNQ1Ai/nYdUBMSkFQVpXETsiZPRbPZcE18AfctdxS/oyTh\n635O6G1tdWaQJHxn98Swfx/y/n1VOrR15mcAuMZPxHPJZZhXLCNh4HnIh2qXSomg+jCtXomkafjO\nOTe0zN9Br6mQvF6y33w77MdWcHLg7aunCwVTzUJCKHUozQzA3649AMZA0fqJSBnpGHftxNurN2mb\ntuMZerW+PL1kOf+SkHJywpwZpV17vH37Y166uESnqjTynZnyPQN4Bl8JgCkghGAsIOUMIKfpjlt5\nnJmS8AYmEEV0pnzIu3chZ2Tgb9s+bLlmj0YLODdKq9YoLcMjc94BF5D9+hRcz06qMVtLI9Q481D1\nODNSWppeh3ZCL6xitw+LzOi9AOUjJavDVTuahnTkCPj9hVZZvvmS6LGjMS/4PbRMSk3FOmcWcmYG\neSNu0COsJVCiM+N0Ol1OpzPb4XDEAN8ATzqdzkMADoejF3Av8BpgR089ux49gnO3w+HoUNZzlPfu\nIWrSsyR2a0/ciGHhK93usg4jAJBlfOfqN1f3DTfj73kOx3YdIu/2u0vd1dezDjgz5Nf3mFavrLpB\nFQXrzM9QY+PIu/k2sj78jJzHnkB2ZWP+cW7VHUdQqzEtXwaAr1fv0LLs16eQ9fZ7pP+yAO/AiyJl\nmqAa8XfpqnceX6RnBciBxpNqXN3oMRNECSqabS66SV7QyfF3PRtMJtRgSl1GxRttSrm5aFH2sGV5\nI/X5TPMf8ys8bsiZSSnf5IG/W/eQsidQqAdNsGZGqyJJX1+g30xla49ONUyrdMGNgs8dAEgSUuC5\nz9+pS+EdJQn3tSNCqV21gVDjzGqKzEQ/9Tixd96CrUBLgJIoOBGQn2YW2chM1EvPk9y+NUkdz8Dw\nrzN/hduN/fn/oVksYT29DLt3YX3/PTRZxj3iJnKe+l/JB9A0rcR/bdq0adqmTZu1bdq0ubnAsmFt\n2rTZ0KZNm5aB94Y2bdrEFFj/Yps2bUaUNrZy3yhNq1dP0yRJ00DTYmI07eabNe3DDzXtyiv1ZU2a\naIJysnixpg0dqmlpaeXbb80a/TMHTVu0qHpsqwpWrNBtvOOOqhvzxx/1Me+8M3/Z/v36soEDq+44\ngtpN9+6aZjRqmssVaUsENc3gwfr3fedOTfv8c/31K69E2qryoaqalpysaS1bFr3+tdf08/riC/39\njBn6+48+qvjxJEnTevcOX75xoz7u7bdXbFxN07T27TUtLq5i+65bp2lduug29O0bvq5HD00zmXTb\nqwJV1bT69fVjDR+uaT5f1Yx7snPTTfpntn594XXB55A33qh5uyrCvn26vcOGVc/4p5+uj5+UpGkv\nvaRp48dr2iOPaNr992va1q2Ft3/iifznuMOH9ddXX109tpUFt1u/LwWv61VX5a+bNElf9uijmubx\naNrkyfr7++7T/x8ypOBIxfoTpQkA1AfmAfc6nc75gWXXA3cA/ZxOZ3A6pw3wpcPh6Iwe7ekNfFSa\npyZPfhM1pR7+3ufhvmY4nsuHhPIijU1akjB7NlnjJ+JJzS5lpJOflJQYUsv6OZzRCaZ+pEs0lOez\na3w6yVF2pNwc0v0y/tr6uTd3kJSUBLNmkzb+eTCW+GdcLPLBA8TefD2+nudi2LUTC5B+5fD88zbH\nEt+pM6bff8fXsxemmZ+TahdiFJGiXN+BiuBykbxuHf7OXcnIVSG3lv79R5BqvwYRxNqzDzHffUf2\n7O8xbN1MFJB+RodaeR8s6TrEnXEW5qWLObbrYKFi+ZiVa7ACx5u1RknNxmywEQe49hwgryLnmZtL\niqbhNVvJLLC/ZE8kGfBu2xG2vEj8fqTcHLTY8ChY0qFDqCn1SK+IXU1bwy8LSWrbCm3PXo4XGCPx\nyFFITOL4MVf5xz2BlJQYUo+5iLp2BPbXXoaZM0kfcQv+s4XCYWkk/rEQKTaOtPrNCz2npAT+z6jf\nFF8p179W3JOM0SQbDPh37iajqm1RVZKOpuppVGlp8Gh4rz33vgOFxJCi9+zHBhw3R6Ng1W3bs4+M\n1GyMf60j6tUXyXnqfyit21SJiaVdA8ucWcQeO0buXfdhWrUc06xZZHz7I/6z2pL47HMQH8/x2+5F\ny/RgqteEeIDJkwHIuPbG0N9ASkrxcg+lPQU+ASQA4xwOxzjAALQD9gCzHQ4HwCKn0zne4XB8AqwE\nfMDHTqez6Dh3ATwXDyL7ramFbmIA/g6dSD2SKQptaxKjEV+37pgX/6F31q2tGI14LhuC7aMZmJYv\nxXdev3IPIR85TNyVl2HcuQPTn+sA8HXsHCqgDeK56j+Y1v+lh8RHjoSv5uY3TasFGNeuRjm9Va0p\nhKzLmNasQlKUsHoZwamDr5+ek21e9Afyvj1oZjP+Dp0ibFX58Z/VFvPSxRi2bsHfrXvYOuOmjWg2\nG0qg27qWoKeZSRkVq5kJqv1p9vC0ZC06BjU+vtS6RsPmf4i9dSTykSOkz1+S3zfE50NKS0Ntc0aJ\n+5eG0qix3phR00LPEtLx46iBGoeqIvfxp1DOakfsbTdiWrFcODOlIB88gGH3LjwDLyzy9zTzs68w\n/zAXX5++EbCuAhgMqPXqIxeo964q5N27kLOz8Fx6Be6rh4HRiGa3o0VFEXvLSL3ORFHCPsewFE1Z\nDtkm79pJ3HXX6IIfJjNZ739S5fYWhfXjDwBwj7wR78WDiLv6CuKu/w/ec85FzsrENeG5kIqkUqB3\nkL91mzI/35XozDidzvuB+8sykNPpfAl4qUxHDZD18cySNxCOTI2T8+jj+Dt2qjXNqIrDM/QqbB/N\nwDJnVrmdGSk1lbirLse4cwfua4ZjmTMLLcpO9rvTC/3N5d1+N54hVxH9+KNYfvgO2ztvkXdvmb4S\n1Y7xr3UkDBqI+5rhZE+ZFmlz6jymlcF6GeHMnIooLVuhNGmK+bdfwOvF37kLWCyRNqvcKIFu6qaV\nK8KdGZcLw9bN+Dt3DT34hGpmKigAIOUGnRl7oXVKk2YYd2wLcyROJPrJ0aEC/ehxY/RnAklCPpaK\npGnlVjI7EbVhQ6S//9IltxMS9QfDrEz83c6u1LhFEWpvsGIpeaMerPLxTybMv/0KgHfAwCLXey+4\nGO8FF9ekSZVGrVcf49bNJf69l4b1s4/RDAY8w68LLTNu2gCAr1t3vJdeHra99/wLsX38Psa1a/D3\n6AmqiuXbbzCtW4NmtYYETNQGDTD+s4m4/16FfOwYamIi5p++R969q/CznqrqtepRURU6hxMx7NiG\neelivL3PQzm9Ncrprcma8Qmxt4zAMv83lGYtyLvptvzDN2kaeu363wtlbo8hmmgIwvD36EnOuGdq\nve8yI1kAACAASURBVCPp63EOSoOGWH74DrzeMu8npaURf81gjP86yb3jbrLfmkrGz/NJ/20Ryumt\ni9hBQq3fgOyXXof69bG/8L9QN/hIE5QcNc/7pUiFEEHpSOnHifvPECwzP8O8fBmaLOPr3jPSZgki\ngSTpEs1uN5Kq4uvRK9IWVQjPBRejxsTqTZOPHAZNw/bOW0RNfrVQ5LHKIjNFPPioTZoi5eVh/eh9\npKNFNxM0bt2M/7SWeM/tg+XXn4m99mqkI0fyZ5brN6iQXSEbgsXPh/TiZ9v77wHoM9xVjFq/Af6W\np2NatbL2dIKvpZh/+wWgzjksJaHWr4/k8SBlVawrCV4v0WMeJnbUXWFNI4NCR0FVzbBdLtAFaewv\nP4/14w9IGNCb2LtuRcrOJmf0k6HnOLV+QySvF+POHeSOegjXsy8iqSq2aW+HjSdlZRI/8DxSWjQg\n9oZrK3YeJ2D9RK84cY+4Md/uiweR9f6n+E9rieuFl8InjUwm8m6+jdy77sNXjLNbFMKZEdRNDAY8\ng4ciZ2SEFIhKxeUifuggjJs3kXfjLeRMeB4kCX+H0iNRWlISvP02kteLbfrUKjiBiiNlpBN77dVY\nvvsWADkzA9Pa1RG1qa5iWr0K88IFxI66C9OqFfjbd6xzcryCqsPXT5do1mw2cuvo7LpWrx45Y8cj\nZ2ViHzcGg3Mr0eOf0Gs60PuwBKl0ZCZHrzs5Mc0MQGmqz7DGPPYgMQ/dW3jfjHTkY8dQWrUma+oH\nePsNwDL/NxL79cT66ce6fSmVa80QVJky7NyBbdrbWD/5EDWlHp4rhlZq3OLwndsH2ZWN8c+11TL+\nSUFuLubFC/GfcSZqoA/eyUDQ8a6oBLJx8yYkjweAmHtux7B9G4Zt/2L7cAZKg4Z677MT8Pbtj697\nT8yL/iDmkfsxbPkH93/+y/Hl68i7Z1S+bYE+OO6hV5HzxFN4rhiK0qgxUdOnYv7+O3C5QNOIGXU3\npkAkKJipUCk8HqxffoaalIRn0AlRpYsuIX3V+iIVQl0vvELOM8+W61DCmRHUWTxDrgLA8u0sfTav\nlHxV85JFGLduwX3NcFyTXi1/9GnwYJR69bH8MKeQ3GdNYlqyGMvv81DrNyD33gcAiLn7NhL6nkNC\nj05EP3Rf2ezTNCxfzzyl++gUPHelSVPc198QQWsEkcZz6RVkv/wGaWs2olVRH5JI4L7xFnxdz8Y6\nZzbRTz0eti4s8mi1okVFVUHNTOE0M7VJs9Brc7AZaQEMgfQypWUrtHr1yJw5m+znXkRyubB9NENf\n17ZthewKogScmdjbbiD6yTFImoZrwnNgNldq3OIIRhosBWbWBeGY5/+G5HbjvWhQpE2pUoL9kCpa\nN2P868//s3ffYW6U1x7Hv6q7q+2216ZjihlMDTiU0HtJIJSEhBBK6JBwIUCA0EtIgAuXHtMCSSgJ\ngQAB04nB9GaKCzYDGIxt3Nbbd9WluX9Io9Xa21d9f5/n4UGWtKMjjcqcec97XgBCBxyEs7ODml8d\nQ/W5Z+EIh+m87qZUc6weystpfeZF2h76Fx233EnLWx/Scec9aySJ/tN/Q+eV19Jx+92Jsi2Ph8Cp\nZwJQe/JxVF9yARX3TqXs+WmEd9mNyLbbpT7bI1H2/DScTU0Ejz426yW7SmakaEW3/z6xDTbE+8Jz\njNt6EvW77djv/Z3JhaTCe+0zvDI6ezSouXnwo0FZ4PpmAQCdN9ycmOO06SScK1fgXPodzqYmKh7+\nO1W//92A23HP+oSa35xG1aUXDXjfUuVcnkhmWp96juaPPyN4wvDW+5US4XYTPP7E4l8Y1emk4/9u\nx3K78c54FSv5fWeVla3RcCdeV4+zebhzZvyJ7faWzCRL2AAsp3ONBf9Sycymk1IxB085g5ZX3sD/\n67NpeWE64X0PGFZctthmRuLxfZV0nXcBTR/NJfSTn41om/0J77k3ls+H9/lpibkTsoayp58EIJhc\n3LRUdI/MDC+Z8XySaETUddnV+E//De4vTDwfvEfoh4euMVemB6eT8IEHE/zl8X12J4tvtHFipCYt\noQj86mT8pyQWUi974jEqr76c+LgGOu55AKumFkc4PGAJv6OlmdrDDobzz1+jvM712Vwqr70KgOBx\n2T9JqGRGipfDQeiwI3EmSx2cba3Qz9kE56pGAOLjGvq8z0DsFbPLnvz3sLcxUq6vE8lMbONNoKKC\nlnc+YtWSVTR9uYimT+cT3Xwy5Q//DUdjY7/bcSe7uHmnv9zv61bKXEsTyYx9BlekVMS22JLArxOl\nJtHtp9D6+NO0vLLmKvVWXX3vIzN+P5VXX47L/LzPx0iVmfnWTGbCBxxEePc9ia2zLs7ODpyLF/W4\n3b3gq0Scm/acqxjbfDJdV12bWNhzhKLbf5+WF1+l+eO5+H9/eaJcOJsqKgjvvR/urxfg+vKL7D5W\nMerspOyVF4luOonYllvlO5qMspMZ96xPcC34csh/7/7kI+KVVcQmbUbXFdcQ3msfYuMn0HndkPpq\nDV5lJV1/upGOG27GEYlALEb7PQ8Qn7BW6uSE3eCjL96XXsD77ttw882M+cEUyh59BOJxHCtXUnfU\nYbgWL6Lr4suJbbxpdp5DGiUzUtSCyVIzm32g3xtHBpKZ6JQdiG0wMTFBL3lWMtdcXy/AcjqJ9VZv\nXFVF8OhjcVgWZclJln1xz5kFgCMQwPvqK1mItPDZZWZxJTNSgrrOv4jg0b/Ef/5FRPbcm9jmk9e4\nT7y+Hmd72xpNRHx334nvz7dRMfX23jfu9+NcmljxvLeRGWvMWNqemEbgpESnIve87tUaKu67C99t\n/wesmcxkWnT772PV1mX1MdKF994XAM+7GZhzUGLKn34SRyCQKBEv8CZDQxWfkCgz890zlbr99uyz\n6UVvHE1NuL4wiW6f7DTo8dD2r6donjkn679NwZ/9gvDue9H5xxtSrbBTycwAJzm9byfLR087DUdn\nBzVnn0ndj/ZLtn9upPPqP+E/94J+t5EpSmakqMW22prQ/t0TyNz9nBGxR2ashuEnMzgchI74Cc6u\nTrz/fWn42xkB94KvEvXofdR9hw86GADvi8/3v51Zn6bKT8qm/SezQRYJ5/JlxOvqoKIi36GIZF5F\nBR2339XrJFubvb6Do627TMTR0kzF1MSidZ6PZ+J57x0c6RObLYvaY35K1R+vTvyzlwYAttgWiXkv\n7nlzAXB+uzBV2hpvGJ+aa1Aq7DlJdhcqSbIsyh+4D8vlKsm5iemd95xdnfhuHfyIiuftN3BYVs91\ndRwOKC/PZIi9q6yk7YlnCJ5yRuoq+/PcbzJjWXjefjNRTnrXXTS/PZPgYUfi+WgmnlmfEDziJwRO\n/3W2o09RMiPFzeGg/ZHHaX30CaC7Drs3zlWrAIiPcGJvMFlqVp6HUjNHRzvOxpXENuq7+1ps402J\nTtosMa+nrxahoVCiLep22xPdaGPKXn4JAoEsRV24nEuXalRGRjX7jLLryy9wtLUC4Pvz7Tjb27Dc\nbtzm59T9+CDG7LVz6nbPO2/hfeet1DZ6G5mxRbdIlBPZIzMV990FgP+0M2md9mLJnaGPbWYQr61T\nMrMa98wP8MyZRfigHxFfZ918h5Nx8YbueXaWy0XF3x/AufCbQf2t9/UZAIQLZJHQ7pGZzj7v41z4\nDa4li4n8YLfEwpzrrU/HfX+j+dW3aZ7xLh33/HXQa8RkgpIZKQl2TWb/yUxj4iz8CDvZxLbYkujm\nkyl7fhrVZ59J5TVX4P7g/TUmuGaD65uvEzEkV/DuS3SLrXAEAqnRqNW5P5+HIxoluvX3CB96OA5/\nV2Il4SLn+urLft8DPXR24uxoVzIjo1oo2VWq/scHMtaYiPf5Z6n4y93E1lo7VSIG4GxqovLKSwHw\n3X5zj230l8zE116HeF0drnlzcbS2UPHwg8TWXoeuK6/NSS19zjmdRHbYEde3C3uOZo1y9ho/6e+p\nkpJ2XNH5x//FEYlQecPg2gt733iNeE0t0W23y1Z0QzKYMrOKv/4FgPB+PZt0xLbaOjUam0tKZqQk\nxNffAKusDNfXX/V5H+eqxhHNl0lnNwIof/QRfHfeSv0h+1N91ukZ2XZ/3B8m1pOJ9tG1xJZaLC5Z\n0746e3JqdPPJhA49DCjyUjPLwnf9tYzZZQpjdplC3T67UXHHrf0eTLiWJxbS0+R/Gc0ie+yVuuyI\nx6k5/UQcfj/+cy8gsnP3ApvxsWPx/vdl3HNm4X1teo/v0t4aAHRv1EF0i60Sa73cMxWHvyvRFtbj\nycbTKQiRXfcAoPp3Z0MolCjJef211MjWaONobKRs2n+ITtqMyG575DucrOm84g90XnplojX61ttS\n9uTjuObO6fdvHCtX4vp2IZGddga3O0eR9m+gMjPniuVU/O0vxNZdj+BRR+cytD4pmZHS4HIR23Qz\n3J/N7bF6bkoshqOpKWPJTPDIo7C8XiI77ETbg48mSrX+8wSOzo6MbL9X8TgVD9yL5fEQPvTw/u+a\nHMZ3Lu19DRmX3Ulok02JbvO9RIvrl1+EYDCzMeeI+4P3qbz5f4ltOJHQAQfh/nweVX+4gvqD9u7z\nOdmJnp34iYxKLhdd5yZaucfr63GEQsQ22JDgL48nsvseRCdvQcdNtxH5wW64Vq5ItX3vuKF7dKa/\nkRmA6BZb4rAsfHfcQryyKietWvMpcOIphPfYm7KXXqDmxF9Sfv891B11GJV/uCrfoeVOPI6jox2A\n8n88iCMcJnDiKSVXVpgucNY5BM45H5xOui69EodlUXXVZbg/eB/vc9N6rd5wf5HoFhjbonC6uw1U\nZuZ96QUcwSCBX/9P1tePGSwlM1IyOq+6Fjxeak45HudqXc0czc04LAsrQ8lMfMOJNL/7Ma1PPUf4\noB8SOvxIHNEonrffGviPh8kzYzruL78gdPhPekw27DW+tZMjM8v7SGaSa9XENtk00dTg0MNxdnbg\nff21zAadI7677wSg47aptD/8GE2ffUXgF8fi+m4Jvttv7rVVpnvWpwB99uYXGS38v7uYpplz6Los\nMaG/6+LLwevFqq2j5fX3CB5/IpEddwLA8+H7RLbahvAhP079veXz9bt9+0DNEQ4TPPaEnHYXywuf\nj7aHHiW8z36U/fdlqi+5EAD3xzPzHFjuVNx1J+M2WQ/3nFlU/P0BLF8loZ/9It9h5Uxk730J77YH\n3jdeo/6Q/ak98ZdU3HcXjuYmvNNfxnfjdVSfcgLlDz4AQDS5JlIhGKjMzP1ZYrSpxwK8eaZkRkpG\nZM+96bjtzzgiEaquurTHomXda8yMy9jjxdfv7igW2XMfADxZXEzTd29i4mzgtDMHvG9s7cTIjKvP\nkZkFWGVlxNddD6C71OyZpwDw/vclyh/++4hjzgXn8mV4X3iWyDbfI/KDRFmMVT8G/yVXYLlcVN50\nPfW77UjZo4/g+nx+6n3hfet1AMK7lm7Zg8igeDzEN9iQ4LEn0PTBrF4XlozssFPqcuB/fgsOB52X\nXZVYKLKufo37p4sma+gtl2tQ318loaKCtr/9o0e3zZx0pyoQVVdfBkDNicfhWrKY4FFHr7Fga0lz\nOGi//0E6L7sa/ymnEx8zhsqrL2fc5htR+4ufUnnjdZQ/8xTl/0ksIhozNs9zwN26y8x6H5lxfzYX\ny+UiaqzZ6j1flMxISQn9+AjCO+9C2YvPU3nphalh3UwsmNmfyPd3xPJVUvbCc9DZdweQAVkW1b85\njfqdt0sMSye5vjDxvvpfwjvvMqhJgvF1EvNA7HVUVn8M14KvEk0Ekt1GottNIbbuenhfegGCQap+\nexbV5/0PjmQHuELmeftNHPF4Yh5TWglDfMJadF16VaJDTFk5NWefyZg9dqLukAPwvvAcnvffJbr5\nZKwJpdUaVmTYHA7iE3vvlBjdelvi1TXEJm5EKFnmGjj7PNoef3rArkXRyVsSGz+B4DHHJU4CjRbl\n5bQ/8DDt9/2N+JgxOJctpeK+u3B/9GG+I8u6eF1i9M21aCEAgZNPy2M0+WHVjyFw9rl0/elGOm+4\nGbxewrvuTtf5F9H2yGNEJ3dPlI9uWjgVAv2OzMTjuOZ9llgfqoCS88KYbSSSKQ4HHff+ldqfH4Hv\nL/fgbG6m4467ca5MTATPVjKD10vgpFPx3XkrVZddROetfx7eZl58nvLHHwWg5rRf0fbYf/DOeBXn\niuUABE4bXN92uwytt2TGsXIlzs4OIumdhBwOQocchu+eP+O79SZcydfL+/qrvZ6lLSSed98BILLL\nrmvcFjjrHAJnnYP745mUPfVvXF8voOyVl6g9IVHuEC7hyagiGeX10jrtJazq6qFPVK6ooPmTeTlt\n1VowysoIHXYkFffdjeeD96i69CLCu+9F2xPPdN8nFMIRDJRE+V3Z00/ifekFnK3dzQ5C+x/Y64Kt\no0nosCMJHXZkj+vcH32Ie35yMdkBSjVzqb9kxrnoW5ydHYS3LJw5PqBkRkpQfK21aX36BWqPOYry\nJx/H0d5GdLspids2yN5Zwa7fX4bnjRlU/OMhInvvS3z8BLzPPUPXNdcN7ke8q4uqSy9MTPDfYy/K\npr9C7U9/jCO5Vkxsgw0JH/yjwQXj9RJvGI9r4Td4XpuO69uFuBZ9i3PRt7iTncxWb+8c+vHh+O75\nM5U3/2/3Zl79b+EnM++9jeWrJLr1tn3eJ7r994lu/30AXPM+w3fHLXinv0zoyKNyFaZI0RtRy9US\n7l42GLF11sF+Bdzz5iTKXR0OHKtWUXfkj3A0N9M86/PECvBFyv3px1T/+lQckQiQGJ1xdHXh/+3v\n8hxZYYrsuQ/cfCNWge3z/srM3J8lFr+NbrF1TmMaiJIZKUlWXT2tjz9N7QnHUPbfl3HPngVAbMO+\nF5scMa+Xjnvup37f3ak6/xyc7YkVtYPHHD+og4DKW27EtWQx/nPOJ7bhRMqmv5JKZAACJ58+pB86\ny+vF9d0S6n5+RM/ry8oSLZnTJvACRKfsQNTYHLf5OZbPh+Xz4Z3xKkSjBdMysodAgMqbrsf9hUl4\nr30GHWNsiy3puOsvWQ5ORKRbfJ31UpedTU04VyzH8nqp++mPcX8+H0isIxbbdFK+QhwRR1srNaf8\nKpXIAHRdeS3Bn/ysoMqRCklk513ovOqPvVYV5FN/IzPuubMBiG5VRMmMYRge4AFgIlAGXAvMA/4G\nWMBc4DemacYNwzgVOB2IAteapvls9sIWGYTKSgKnnYH3jddSZVOxLNdrxzaZROefbqT6t79JXeds\nXEmMfpKZWAyX+TkVU28ntv4GdJ17Ae45s1M3xyurCP3saALH/WpIsUSm7IDruyUEjzyK8D77Edtg\nIvGJE4mPn9D7SJHTSeu0lyh7bhqxddej7PlpVPztfsoef5TQL44FkmfefnsWHbf9OaMLfDmamrBq\naoZ09rb88Ufx3XELkJgrJSJSqOx5jDbPO29RMfUO3PPmElt/A1yLF+GaNxfPm6/ju/l/aZ32Up/z\nlwqOZVF9zm9wLVpI13kXUHnzjUCyQ5cSmb45HIn2xgWmv3VmUiMzWxZWMjNQ7cuxQJNpmrsDBwF3\nAjcDlyWvcwCHGYaxFnA2sCtwIHCdYRiF0XxaRrXo5lukLsfWXicnX6zBXxxLMK0FpT1fp1ehEGN2\n2Ib6vXfBEY3S+acbwecjtln3ZMDQz3+RmDxYVTWkOLqu+ROt/36GjrvvJ/SzXxDd+QeJNVX6KXmz\n6uoT60vstQ/+3/4Oq7ycyv/9U2qtloq7Ej++VeefA2mjRoMSj/faUMD1hcm4yRtR+Ycrh7S5sicf\nB6D53Y8IHlva61aISHGLpY3MAFSf82s8sz8lcOwJdNx4KwCeObMTcxZXLE987xaJivvuouz5aYR3\n3R3/BZfQ8sJ0/GefR3TKDvkOTYahv3Vm3PPmEh/XUHCNcwZKZh4HLk9edpAYdZkCvJ687gVgP2BH\n4G3TNEOmabYBXwHbZD5ckaGJr7c+8eRZhliuznI5HHTccTdtf30EAOfKlX3e1T13Nq4li3FYFsGf\nH0P4wIOBRBeUeMN4AKLDXEwrvs66PVb3Hs7fB04+Hdd3S6j4619wtLVS9nxiwNUz+1PKnn16SNsr\nf/jvjN16Eq7kmR2b78brEv9PrhUzGM7vluB95y3CP9iV2CbFWZYhIqOHVduzLbEjFCL482PovOm2\n1Flu323/hyvZtKXsiccSreQLnWXhu+1m4nV1dNx9P7hcRKfsQNdlV43Ohg+loKwMy+XC4ff3uNrR\n3oZr0bdEC2zyPwxQZmaaZieAYRjVwL+By4CbTNO0F/DoAGqBGqAt7U/t6/tVX+/D7S6siU+FrKGh\nOt8hFKettoT338drTBrxazikv5+cmGBf1dVKVV9/93mynOzhhyn/5S/pMW605RYwYyXVu+1Edb72\n/TVXwMN/o+q2m6jyOiAUgkMOgWefpcacCw1DGBH58B2IxRjz8buw1w9g3jy47jpIrm3DuHEDvr6p\n2/+WSKq8Jxynz0WO6fUuDNoPhWHQ++HAvWHHHeGMM+CRR2CTTSifOpVylwvG1/S87/XX4/j97xlz\n6w3wxBOZDzqTFi+GxpVw5JGM3So/J5b0WciCqio8oUDP1/bzxELT3h2mrPGa53sfDDhj1jCM9YGn\ngKmmaf7DMIz/Tbu5GmgF2pOXV7++Xy0t/oHuIkkNDdU0NnbkO4yiVLWpQcX779M1YV38I3gNh7oP\nnJ4qxgLBhYvp6OPvql9/k3KgadJWxFe7T9lRx1DmKaN93U0gb/veQ8VZv6Xqj1fDxRdjlZXRctEV\njHn2WUKz5tA+hLjqP/kUNxB+9nnir79F2bT/4LAsopO3wLlqFc7GlaxasKTPhdXSX//6vz+Ey+Oh\naa8DsfS5yBl9DxUG7YfCMOT98Ox/E/8/5KeJ/zd3HwPVb7EV7nlz6broUvwnnknd4//G8+STtLzy\nOtHvbZ/BqDPL++pb1AKdk7cmkIf3pD4L2THGVwlt7TSnvbYVL79KFdC+0WaE0q7P1T7oL2HqdwzQ\nMIwJwMvARaZpPpC8+hPDMPZKXj4YeBP4ANjdMIxywzBqgckkmgOI5F10q0TFY3QzI6ePa5eJ9Vdm\n5pn5IfFx43qd6Bk66mjaH3kcyvI7/Sxw6pnEkuvWBI85jpixOfFxDbi/MAe/kWAQ11dfAuB9bTrl\nzzxFdJvv0fb3f9Ly2jsED0tM4Hd98/WAm3J9Ph/3Z3MI77Mf1pixQ39CIiIFpuP2qXTceCv+8y4E\nh4Oui68AoPK6P/T5N+6PZ+Ka91muQuw9hlkfA2S0IYzkn+Xz9WgA4Fi1Ct8dtxKvriG81755jKx3\nAxU0XgLUA5cbhjHDMIwZJErNrjYM413AC/zbNM3lwO0kEptXgUtN0wxmL2yRwQseewJtf/k74R8e\nmtsHLi8nXlOLs7H3ZMa5fBmuJYuJTNmhx8r1Bcfno/OPNxDdYiv8Z58HQHTSZjgXfZtqDNCvWAzP\nu2/jiMdTV1nJzmnhg38ETmdqzRvX1wsG3Jw98b/Q178RERms6DbfI3jCSanfgsgeexHefU+8r03H\n/f57a9zfuXgRdYcdTPVZp+c61B48sxKlR9Ft+l7nS4qPVVODs3EltYcdDLEYldddg7OtFf+FF2M1\nZGnx8REYaM7MOcA5vdy0Zy/3vQ+4L0NxiWROWRnhPLXujY8fj7Ox925m7pkfAhD5/o65DGlYwj8+\nosdrGJtk4H33bVwLviI2wGTAqgvPo+Khv/a4LvSzX/ToLDfoZMayKH/y8US76gMOHuKzEBEpHv7z\nLsT75utU/P1+OnbaucdtlX+8GkcohGv50jxFl+CePYvYBhtqlLzEdF10GVVXX4733bepuPvPlD/8\nd6KbTyZw0mn5Dq1XajUhkkXxhvE4mpoSC0+uxvNRIpkpxvaVsUmJiZ7uL3uWmjlXLMfR0tz97++W\n9Ehk2h76F10XXUrHn27sub2NBpfMuD/8ANeibwn/8BDw+Ub0HERECllkl92IbrQxZc89g6O9u8eS\n++OZlCdHqB0tLWBZfW0iqxxtrThXNea8hFuyL7LPfnRe/UcAqq6+DIdl0XndTUNaCy6XlMyIZFF8\n/AQcloV79qdUXnMFZU8+jqOxEQDPzA+wnE4iBTy5sy/2PCTfLTfiXLE8cWUkQt2+u1P708NSP64V\n90wFoOu8C+j4v9sJH3AQ/vMvWmPNnPj6GxCvqsY984N+H7f8yccAEqtKi4iUMoeD0NG/xBEI4H1u\nWuI6y6LqykuBxNppjlisR6KTS/bJJ3tkXUpLZNfdiY8ZA0DwiJ8Q2XX3PEfUNyUzIlkUX2ttAGp/\nfiS+O2+l5oyTGbflJtTvvSvuTz4iNnnLIS+GWQgiu+xG4OTTcM+fR92hB+Jc9C2ed97CtXIFnjmz\n8LzzFo62Vsof+huxtdbGf95FBI/7Vd9zg9xuIrvtgfubr3Eu/KaPB41Q9vSTxMc1jGj9HBGRYhHa\n70Ag0SwGwPvcNDzvv0vooB8R2XNvABzNzX3+fcYFApQ9/STOZUu7k5mNlMyUJI+H4DHHEx/XQNeV\n1+Y7mn4pmRHJosCpZxDdZFOcba0ETjiZzsuuIrz7Xri++gJHOEx4tz3yHeLwOBx0/ulGus67ANfC\nb6g75IAeK1ZX3H8v5X//K86uTgKnngle74CbDO+d6JDinfFq95WxWGJtG4D//hdnUxPBw48E94Bd\n5UVEil7M2BzL68U951MIBqm65nIst5uuK68hXp84a+5syUEy4/dTcdedjP3+1tSc+isqr75MIzOj\nQNcV19A05wvi66yb71D6pSMCkSyKb7AhLa+8gWf2p0R23gWcTgJnnweBAO55c4luvkW+Qxw+hwP/\n7y/Hqq2n6spLcC1fhlVRQWzjTfG+8Cye994mXlVN8IQTB7W58F77AIlkJvirkwGoOfVXuD98n5YZ\n78K//gVA6IifZuXpiIgUHK+X6OQtcc/7jMprr8S18Bv8p/+a2CaTUiVA2U5mnMkTVq6VK4hX+cy3\nSgAAIABJREFUVmGVl+P+5GNwJM6HxzbaOKuPL3nmKvzF7TUyI5JtVVVEdtkNnGkft4qKxMT/ysr8\nxZUhgTPPovXRJwjvshtdF1xC4NQzcMRiOFetInjcr/pcBHN18Y02Jrb2OriTjRE8r75C2bNP41qx\nnMprLodp04ittXZRNkwQERmu6Dbb4giH8d17F9GNNqbr95cDYCVHZrJdZlZx/724Vq7Af8rpNH88\nl8gOO+H+5mvcc2ZheTzE11s/q48vMhAlMyIyYpF99qftP88TOOscgkf8lHh9PZbbTeC0M4e0nei2\n38O1YjnO75ZQdfnFWE4nsQ02pOKfD0Nzc2pdGhGR0SK6dWINF8vppOOOe1InwXJSZhaNUv7EY8TH\njKHrqj9i1Y9JNYBxf2ES23Ciyn4l73RUICKZVVFB298fpf3BfxJfd70h/Wl0m+8BUH3e/+D+8guC\nx51I24OPpm4PHXxIRkMVESl0kd32wHK58J97AdEdd0pdb41NrO3iaG7K2mN73piBc1Vjorw3Ofcx\nuvU2PWITyTel0yKScdGdfzC8v9s2kcx4X5tOvLaOrosuxRo3jpaXZ1D//pvqYiYio05s00ms+nLx\nmi3t7ZGZLJaZuT+bC0B4z31S19kjRQD+cy/I2mOLDJaSGREpGPbIDID/gt9jjRuXuP5728P+e0Jj\nR75CExHJn15a+FvJBgCOlpasPaxr0bcAxDbYMHVdbNNJiXVHfrAb8bXXydpjiwyWkhkRKRjxCWsR\nNTYHl5vAiafmOxwRkYKVi5EZ1+JEMhPfYIO0K1103PPXrD2myFApmRGRgtL6wnQshxM8nnyHIiJS\nuLxe4pVVOLLYAMC56FviY8ZgVVVn7TFERkrJjIgUFP1oiogMjjVmDM5sNQCwLFxLFhM1Jmdn+yIZ\nom5mIiIiIkUovtbaOFcsh1gs49t2rlyBIxgkvv4GA99ZJI+UzIiIiIgUodh66yUWKV6xPOPbdvYy\n+V+kECmZERERESlC8XXXB8C5ZEnGt53qZKaRGSlwSmZEREREilBsvUQy4/puceY22tmJ+713Kf/n\nI4nH2GLLzG1bJAsG1QDAMIydgBtM09zLMIxHgbWSN00E3jNN82jDMG4DdgPshSAOM02zLdMBi4iI\niAjE11sPyOzITNUfr6Li/nsBCO+5N5Gdd8nYtkWyYcBkxjCMC4HjgC4A0zSPTl5fD7wGnJu86xTg\nQNM0V2UnVBERERGxxdbN8MiMZeF9/lkAopM2o/P6m8DhyMy2RbJkMGVmC4Aje7n+auAO0zSXGYbh\nBCYB9xqG8bZhGCdlMkgRERER6ckemal44D5qjzoM4vERbc/1+Xxcy5YSPOIntLw9k9gmkzIRpkhW\nDTgyY5rmE4ZhTEy/zjCM8cC+dI/KVAJ3ADcDLuA1wzBmmqY5u79t19f7cLtdw4l7VGpo0Pob+aZ9\nkF96/fNP+6AwaD8Uhrzvh3FVqYve11+jwRWBceOGv72/vg5A+RGHUZ7v5zZIed8Hkvd9MNxFM38K\n/MM0TbuxuR+4zTRNP4BhGK8C2wL9JjMtLf5hPvzo09BQTWNjx8B3lKzRPsgvvf75p31QGLQfCkOh\n7IeGtMvN5kJiVtmwt1X7zLN4gVVTdsUqgOc2kELZB6NZrvZBfwnTcLuZ7Qe8kPbvzYC3DcNwGYbh\nIdEI4ONhbltEREREBqHtkceI19UB4FzVOOztODra8bz/LpHttsdqaBj4D0QKxHCTGQP42v6HaZrz\ngYeA94DXgQdN0/xs5OGJiIiISF/C+x+E/4KLAXA0Db8Hk+f1GTiiUcL7HpCp0ERyYlBlZqZpLgR2\nTvv3Gk3HTdO8EbgxY5GJiIiIyIDiYxPzZJyNwx+Z8U5/GYDwvvtnJCaRXNGimSIiIiJFLD4uURbm\nHOLIjHPRtziXLE60ZJ7+CvGxY4l+b/tshCiSNUpmRERERIpYamRmiHNm6vffg7Hbb4nn/XdxLV9G\neK99waUus1JclMyIiIiIFDF7ZMbR1DT4P7IsnC0tANT9+CAAwvtpvowUHyUzIiIiIkXMGjsWGNrI\njKOrs+c2nM7EyIxIkRnuOjMiIiIiUgjcbuL19UOaM2OP4oT32JvQDw8hvs66qaRIpJgomREREREp\ncvFxDUMamXE2J5KZ6BZbEjzp1GyFJZJ1KjMTERERKXLxseNwNDdDLDao+9ujOPFx47IZlkjWKZkR\nERERKXJWw3gcloVz5YpB3d8uM7PGqLRMipuSGREREZEiF91yKwDcn34yqPs7m5sBiCuZkSKnZEZE\nRESkyEWm7ACAZ+YHg7q/PWfGXqNGpFgpmREREREpctHtp2A5HLg/+nBQ93ck58yog5kUOyUzIiIi\nIkXOqq4htvlkPJ9+DNHogPd3JufMxMeMyXZoIlmlZEZERESkBESm7IDD78c1f96A93U2N2E5nVh1\n9TmITCR7lMyIiIiIlICoPW9mEKVmjqZVWGPGgFOHglLc9A4WERERKQGR7+8IDNwEwNHWinPFCnUy\nk5KgZEZERESkBMQmbUa8uqbfJgCOpiZqjzwUZ0c74f0OzGF0ItmhZEZERESkFDidRLefgnvBVziS\nrZfTOVaupO7IH+GZM4vAsSfQdcU1eQhSJLMGlcwYhrGTYRgzkpe3MwzjO8MwZiT/+3ny+lMNw5hp\nGMZ7hmEcksWYRURERKQXqfVmPvmox/XOZUupO/xg3PPn4T/ldDpvuk3zZaQkuAe6g2EYFwLHAV3J\nq6YAN5um+X9p91kLOBv4PlAOvGUYxiumaYYyH7KIiIiI9Ca6Q2LejHvmh4T3PQAAz2vTqb7gXFyL\nFuI/67d0XX41OBz5DFMkYwZMZoAFwJHAQ8l/TwEMwzAOA74EfgvsCLydTF5ChmF8BWwDDG7lJhER\nEREZsch2U4DuJgDu2Z9Se/SRAHRdeAn+8y9SIiMlZcBkxjTNJwzDmJh21QfAX0zT/MgwjEuBK4FP\ngba0+3QAtQNtu77eh9vtGlrEo1hDQ3W+Qxj1tA/yS69//mkfFAbth8JQkPuhoRo22wzvJx/RMLYS\n5s8Cy4J77qHytNOozHd8GVaQ+2CUyfc+GMzIzOqeMk2z1b4M3AG8AaQ/k2qgdfU/XF1Li38YDz86\nNTRU09jYke8wRjXtg/zS659/2geFQfuhMBTyfqj+3hTKH/snuFxEdtgJD9CyyWSiBRrvcBXyPhgt\ncrUP+kuYhjPz6yXDMHZMXt4X+IjEaM3uhmGUG4ZRC0wG5g5j2yIiIiIyAqHDjkhd9nz4PgDRjTfN\nVzgiWTWcZOZM4JZkd7NdgWtN01wO3A68CbwKXGqaZjBjUYqIiIjIoIT3P4hVXy3GSs6Nia27HlRV\n5TkqkewYVJmZaZoLgZ2Tlz8mkcSsfp/7gPsyGZyIiIiIDJ1VU0tsi61wfzaH2KaT8h2OSNaowbiI\niIhICYrsuBMA0c2MPEcikj1KZkRERERKUHivfQGIfm/7PEcikj3D6WYmIiIiIgUufNAPaZn+JtEt\nt853KCJZo2RGREREpBQ5HES33jbfUYhklcrMRERERESkKCmZERERERGRoqRkRkREREREipKSGRER\nERERKUpKZkREREREpCg5LMvKdwwiIiIiIiJDppEZEREREREpSkpmRERERESkKCmZERERERGRoqRk\nRkREREREipKSGRERERERKUpKZkREREREpCgpmRERERERkaKkZEZERERERIqSkhkRERERESlKSmZE\nRERERKQoKZkREREREZGipGRGRERERESKkpIZEREREREpSkpmRERERESkKCmZERERERGRoqRkRkRE\nREREipKSGRERERERKUpKZkREREREpCgpmRERERERkaKkZEZERERERIqSO98BABiGsRNwg2mae/Vx\n+0HA75P/dAC7AVuZpjk/NxGKiIiIiEihcViWldcADMO4EDgO6DJNc+dB3P8CoN40zUuyHpyIiIiI\niBSsQhiZWQAcCTwEYBjG1sDtJEZgmoCTTNNsS962HonEZ4f8hCoiIiIiIoUi73NmTNN8AoikXXUf\n8JtkydnzwIVpt50H3GKaZih3EYqIiIiISCEqhJGZ1U0GphqGAeABvgQwDMMJHAJcmr/QRERERESk\nUBRiMmMCx5umucgwjF2BtZPXbwV8bppmIH+hiYiIiIhIoRh0MmMYhgd4AJgIlAHXmqb5TNrt5wKn\nAI3Jq043TdMcRkxnAg8ahuEGLOBk+yGAr4exPRERERERKUGD7mZmGMaJwLamaf7WMIwxwKemaW6Q\ndvvDJOazfJSdUEVERERERLoNpczsceDfycsOILra7VOAiw3DWAt4zjTN6zIQn4iIiIiISK8G3c3M\nNM1O0zQ7DMOoJpHUXLbaXR4FzgD2AXYzDOOQzIUpIiIiIiLS05AaABiGsT7wFDDVNM1/pF3vAG5N\nWw/mOWA74Nn+ttfY2JHfFTuLSH29j5YWf77DGNW0D/JLr3/+aR8UBu2HwqD9kH/aB/mXq33Q0FDt\n6Ou2oTQAmAC8DJxlmub01W6uAeYahjEZ6CIxOvPAMGKVPrjdrnyHMOppH+SXXv/80z4oDNoPhUH7\nIf+0D/KvEPbBUEZmLgHqgcsNw7g8ed19QKVpmvcahnEJ8BoQAqabpvl8ZkMVERERERHpNuhkxjTN\nc4Bz+rn9IeChTAQlIiIiIiIykEE3ABARERERESkkSmZERERERASAWDxGIBrIdxiDpmRGREREREQA\nOOOVk9nvsd3zHcagKZkREREREREAFrZ/w7ftC/MdxqApmREREREREQDiVpyoFc13GIOmZGaYzjrr\nNL79dmG+wxARERERyZi4FSduxbGs4ljbXsmMiIiIiIgAELdiyf/H8xzJ4Axl0cycu+qdy5i24D8Z\n3eahmxzOVbtc2+ftl1xyAUcddTTbbTeFzz+fx/3330N1dQ1Ll35HLBbj6KN/yb77HpC6//3338PY\nsWM5/PCf8u23C7nxxj9x5533cvzxP2fbbbdnwYIv2XDDidTXj2HWrE/weDzcdNPtBINBrr/+Gtra\n2gD47W8vYJNNNs3ocxURERERGQo7iYlaUVy48hzNwDQys5pDDz2cF154FoDnnpvGzjvvQl1dHXff\n/QC33TaV++67i9bW1gG34/f72X//A5k69S/MmvUJW2+9DX/+831Eo1G++WYBDz74AFOm7Mgdd9zD\nhRdeyk03XZftpyYiIiIi0i87mYnFY3mOZHAKe2Rml2v7HUXJhp12+gFTp95Ge3sbs2d/gmXF2WGH\nnQHw+SqZOHEjvvtuSa9/u3ptoWFsDkBVVTUTJ24MQHV1NaFQmK+//oqPP57J9OkvA9DR0Z6tpyQi\nIiIiMiixVJmZkpmi5HQ62Xvv/bjppuvZffe9qK+vZ/bsT9hzz73x+7tYsGAB66yzTur+Xm8ZTU1N\nAHzxxeerbc3R5+NsuOFEDjhgCw444CBaWpqZNi2z5XQiIiIiIkOVKjOLF0dHMyUzvfjRj37Mz352\nGI8++hTjxjVwww3XcuaZJxMKhTjppFOprx+Tuu++++7PFVdczCeffIRhTB70Yxx//Elcf/0feOaZ\nJ/H7uzjppNOy8VRERERERAYtVWZWJA0AHPlsu9bY2FEcPd8KQENDNY2NHfkOY1TTPsgvvf75p31Q\nGLQfCoP2Q/5pH2TH9g9uyZLOxcz51ZdM8E3o97652gcNDdV9ljupAYCIiIiIiADdIzPxImkAoGRG\nRERERESA7gYAsSJpAKBkRkREREREgOJrAKBkRkREREREALBIlplpZEZERERERIqJvVhmsXQzUzIj\nIiIiIiIAxEk0G1aZmYiIiIiIFJXukRmVmYmIiIiISBFJtWZWMiMiIiIiIsXEbgBQLGVm7sHe0TAM\nD/AAMBEoA641TfOZtNsPBa4AosADpmnel9lQRUREREQkm0q5zOxYoMk0zd2Bg4A77RuSic4twAHA\nnsBphmFMyGSgIiIiIiKSXfHkyEwpdjN7HLg8edlBYgTGNhn4yjTNFtM0w8BbwB6ZCVFERERERHLB\nnjMTK7UyM9M0OwEMw6gG/g1clnZzDdCW9u8OoHagbdbX+3C7XYMNYdRraKjOdwijnvZBfun1zz/t\ng8Kg/VAYtB/yT/sgsyzLSiUz1TVlg3p9870PBp3MABiGsT7wFDDVNM1/pN3UDqQ/k2qgdaDttbT4\nh/Lwo1pDQzWNjR35DmNU0z7IL73++ad9UBi0HwqD9kP+aR9kXjyttKyppWPA1zdX+6C/hGkoDQAm\nAC8DZ5mmOX21m+cDkwzDGAN0kigxu2nooYqIiIiISD7Yk/8BYlaJlZkBlwD1wOWGYdhzZ+4DKk3T\nvNcwjPOAl0jMw3nANM3vMhuqiIiIiIhkiz35H4qnAcBQ5sycA5zTz+3TgGmZCEpERERERHIrvcws\nfZSmkGnRTBERERER6bG2TLGUmSmZERERERERLI3MiIiIiIhIMerZAEDJjIiIiIiIFIn0BgDRIlk0\nU8mMiIiIiIgQt6y0y8XRzUzJjIiIiIiIrNYAQGVmIiIiIiJSJNIbAKjMTEREREREikaPdWY0MiMi\nIiIiIsUiPYGJK5kREREREZFiEe9RZqZkRkREREREioQaAIiIiIiISFFKbwAQUwMAEREREREpFunr\nzGhkRkREREREiobKzEREREREpCjFVWYm6dpCrVzx9iUs61ya71BERERERPrVc52ZeD/3LBxKZrJo\nxuJXuXvWnTz/zbR8hyIiIiIi0q+4yswkXVekC4BQLJznSERERERE+tdznRmVmY16/mQyE1EyIyIi\nIpIRlmXxm/+exoOzHsx3KCUnfTQmrpEZ8UcDAETikTxHIiIiIlIaWkMtPP7Fo9zy3i35DqXk9Jwz\no2Rm1PNHkyMzcY3MSO499/U0zp9xNlZaz3gREZFiF06eJJ6zYg7+iD/P0ZSW9HVmonElM6NeIGKP\nzBRHzeFQ6SC5sJ344i95aN7fWNK5ON+hiIiIZIxdvh+zYsxeNSvP0ZSW+GgoMzMMYyfDMGb0cv25\nhmF8ZhjGjOR/RkYiLGKBaOJsQanOmdn2wc0545WT8h2GDMCBI98hiIiIZEw4reLlkxUf5TGS0tNz\nnZniSGbcQ7mzYRgXAscBXb3cPAU43jRNvauS/HYyU4JzZuJWnOVdy3jyy39z9/4P5Dsc6YeSGRER\nKSXRWHfFyycrZ+YxktLTo5uZVRyVRUMdmVkAHNnHbVOAiw3DeMswjItHFlZpCJRwA4BQLJTvEGSQ\niuXLSERyryvSxfymefkOQ2RI0kdmPl75cR4jKT3pk/5LcmTGNM0nDMOY2MfNjwJ/BtqBpwzDOMQ0\nzWf72159vQ+32zWUEIpK1JE44Hd6LBoaqke8vUxsI1Nag91v8EKKK9uK8bnW1JXRMLb44u5NMb7+\npUb7oDBkaj/c+MofuOmdm1h2/jImVE3IyDZHE30e8qMq7EldXtS+EHxBGiob8hdQCalpK09d9pQ5\nB/Uez/fnYEjJTF8Mw3AAt5qm2Zb893PAdkC/yUxLS2l3oGjzdwDQ4ffT2Ngxom01NFSPeBuZtNLf\nlLpcSHFlU6Htg8FasaqV+njxxb26Yn39S0kh7oNlnUu5Z/ZUzptyATVltfkOJycyuR8WNy/FwuKr\n7xbjHOPLyDZHi0L8PIwWK5paAXA73UTjUV6ZN4P9Jx6U56hKQ3Nr93u6KxAc8D2eq89BfwlTprqZ\n1QBzDcOoSiY2+wCjfu5MoITnzIRiwXyHIINUiu8/EduzXz/N1E9v583v3sh3KEXJXuFb5ahSTKLJ\n37Xt194egI9XjvpDzowpxgYAI0pmDMM4xjCM05IjMpcArwFvAp+Zpvl8JgIsZnbv81I8mAxrzkzR\niMZK7/0nYgsmv4v0nTQ8duvVWIkuISCJA9JpC56mI9ye71AyJpzsErvr+rsC8ImSmYxJX2emWFoz\nD7nMzDTNhcDOycv/SLv+IeChjEVWAlINAEqwNXOoBJ9TqSrVdY5EoDtZL8WTRrlgL4oX1fdEyXr0\n80c4d8ZZ/HCjQ/nbwY/kO5yMsBcjX7tqbSbWbMQnKz7CsiwcDnXvHKn0BgDF8r2gRTOzyB9NdLAu\nxYNJnQUtHjGVj0gJs7saFcuPbqGxX7dS/J2ShLlNswF4d+lbeY4kc+z3q9flZfsJU2gJtfBN+9d5\njqo09CgzK5KRGSUzWeSP2K2ZS28UQyMzhc1KGybWGWspZak5HzoYH5ZUmZlOepSscHL00u30DHDP\n4mFXvHhdXrYbPwXQ4pmZkl5aFktLbAqZkpkssSwr1QAgXIIH/moAUNjSe/BHlcxICbOTdSXtw6Nk\nsPTZvwGeEkpm7N84j8vDduO/D2jeTKb0bABQHN8LSmayJBgLYpE4O16KPxIqMytsoWh3sqnyESll\n9hlaJe3DY3cxK8XfKUmwT6h6XKWTzESSo01el5etG7bB5XDx0YqZeY6qNKjMTFLsURko1ZGZ0ntO\npSSYlmxG1M1MSlj3yIwOxocjlmoAoO+JUmUnqqU0MmN/7r0uLxXuCjao2ZBF7d/mOarSEOtRZqZk\nZlSz2zJDaf5IaGSmsKWXAaoWXkqZfVBTit+zuWAfrESL5KBFhi5VklVSyUzP51TtraEr0pnPkEqG\nyswkxW7LDD3nL2TLqsAqvmz5IuuPYwulJTPpk82lMKQnm5pLIKWsO5kpjh/dQqM5M6XPTvRLqQFA\nOK3MDKDKU4U/6i+aRR4LWfoxnRoAjHLpZWa5KPO55M3fsd/ju9MUaMr6Y0HPZEY/goUnGNX+kdEh\nqpGZEbFHbod7BrYj3M68ps8yGZJkWHdJVukkM/bITHoyA2h0JgNUZiYp6WVmuTgzvsK/gkA0wAvf\nPJv1xwKd+S906WVm2j9SysIxzZkZCftM9nC/J6597yr2fWw3WoMtmQxLMsg+oVVKIzPpc2YAqrx2\nMtOVt5hKhcrMJMWfPjKTgzIzu3vVtAX/yfpjQc8GAJqTUXhCoyDZDEaDXP7W7zXpc5SLqjXziERT\n68wM7wzs8q7lxKwYbeG2TIYlGZTqZlZKyUzyJIbdoa3SUw1Ap0ZmRkwjM5LSc2Qm+wf7dveqN797\nnZZgc9YfTyMzhS2Y1po5WqLdzF5bPJ17Zk/lyS8fz3cokkf2nMRiOYNYaGIjnDNjl1Sra2Lhsn+v\nS6nkONxHmVlnuCNvMZWK9JGZYnnPKJnJkp5zZnIwMpMsK4rGo7z4zfM5e7zEYxZH5j6a9Fg0s0jO\nrAzViq7lQM9RKBl97JNFOqkyPKluZsNOZhLNbvT6Fy67UiQXVSK5El2tzKzSUwloZCYTLK0zk3kz\nl3/QY5SjWPQsM4tkveNXMBqkwl0BwDMLnsrqY0HPMjNNvC086Ytmlur+WelfAeiM8GjXvWhmcZxB\nLDTd3cyG9zmyf59L6UC51NjzSEppfbjVS+eqvCozy5T0BCaubmYjZzZ/zg+f3I/7Zt+V71CGLH1k\nxsLKenYbigVZv3oDth63LW8smZH1yZhhdTMraMFR0ABghZ3MZOH5qd148YhozsyIRFPrzIywzEyv\nf8GyO3zlokokV9ZoAJAsM2sKrKIt1Jq3uEpBPO33r1iO7wo6mVnetQyAr9sW5DmSoQtEEkPvLocL\n6D6LkC3BaIgyVzmHbnIYkXiElxa+kNXH08jM4ASjwby0LR0NDQAaU8lM5j9bRz97JGe/embGtyuZ\nF1WZ2YjE43aZ2fBOuKVGZjRCWrDsfVRKJbn2MdXq3czOm/E/TLp/A603MwJxlZllll2qZZeTFBM7\n9tqyWiD7B/yhWJAyVxmHbnIYkP2uZj1HZorjzZ4Pd316B3v96wc5T8h77J9YcZxZGaoV/sScmXAW\nDqLeXfo2Hy5/P+PblcyzD2p08DI80RGuM6ORmcIWjoVTcyhLaR+tPmfGHpmxdYTbcx5TqehZZlYc\n36uFncwk6zwbA415jmTo7C/4Gm8imQln8UskFo8RiUcod5ezSd0kthi7FTMWv0p7KHutMrWOyeCs\nDCQS8UZ/bt/D6Ytmlur+WelfCWT+REE4FiYYCxKKls5ZzFKm1swjM9KRre4GAKVTwlRK/GnrrpTU\nyEy8Z2vmqmRrZls+WoW/ueR13lzyes4fN9MsdTPLLHt0ozF50FIoDv/PD7n4zd/1ex97WLe2rA7I\n7siMPT+i3FUOwN7r70s4HmZe87ysPabWmRkce9QgnOMfkfRksxT3j2VZqRHbcIYPotqTZ/TS5x1J\n4bL3v8pdhyc+gjkzsXgs9TnRoqWFKX0RyVJK+CN9lJnZsnkyty8/eeZQfvLMoTl/3Ezruc5McTQA\ncOc7gP7YZxRWBRqxLAuHw5HniBJ1we8sfStV4tIXOxGrSZaZZXPOjH3gWuZOJDN2aZs/iyvhqgHA\n4NivUz6TmVL6AbO1hJpTzyvTB7F2eUIpncUsZZozMzJ2mfBwyvQCsUDqcrbnhcrwpCczuf4dyqb0\nBgAhImuUmWkR1+HrMWemSMp3C3tkJtJdi9saym53rsGyz9quHGC0yB56r/Vmf86MXQ5T5ioDwOf2\nAWS1pXX6D5fOyPXNLr3IdUvMng0aSm//rOjqnkeX6TkzdjITjAYGuKcUgu6ktvTe57kQHcGimXaj\nm8TfK5ksRF1prYpLKeG0P/d2a+ZK72plZjkemUlfqLrY9WwAUBzfq4WdzKS1N871nIO+tIUTLf86\nwu39Jgv2qIg9SpLNOTOrl5n5kotH+aPdZ2Tmrpoz6G4zlmUN2Fs8vQRHK2/3rRDKzErxjHV6U5BM\n1+rbJywi8UjRnJUazVLrzBTJj26hSZWZDeN7Iv03ppQOlEtJ+nFUzIqVzHdaOBbG6XDiciY6xq4+\nMpPrMrNSagcd71FmVhzvl8JOZtKGRxsDhTFvJv0D0l9MgWgAp8OZWpU2m/3d7TMCZe7kyIyn58jM\n7MZP2eexXXlk/oOD2t71H/yBte6qS63j0ZueIzOld7CcKXYSk+v5F+mT10uxZWp6mWeEHvX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YF8Uwynz4kEDSsTIDdmYc8My4am69memQATOGmOejYvW7mTy+TcfmPsmTnNifIvYCFOZPkN7b0F\n6ZcBfp7MTF96H4U9Mz+u3ryfIlwiZqYgYSDenf8B0uMyoJH/dHpmSHFYKcHM5HDJTJ21Dmuqg8mM\n9RQF5V6/V8BM9sUAgK8qqe6RLvadDvwiMzuJCDIzeklmpoUnMzvSdhhP/PDPiK5fvUVp2yH4GT/G\npo1DvCaePp6uT6fJTDjpE5+ZIUmFWIbi9ruxcMV8/DqGZi9C8UlVFciFoxEwM+FlZhGZGScbJJKF\nwCXh6EP2F0dlZqeWmWnnEpRuUTITYAIwd5gxOKkQGRyt3OYIZWaItG538w8hz51K8OVPp7MaRs41\nnVIHlVwZk/yDzFKScug7WSDFCIeELIs/owkI9n4JemYchJlJO2lugQSkcBCUWkZjZojM7OcR/J5t\nEBdS+nMeCJiZn0kPVH/nzDgEc2Z+/EHyjx0uvxM6ntycD9VPyM2MFIel3MxIQXZ741aYO8tRnMza\n5p8qZobcc2WQcX/3fv1o4yczEZQLpwOMSGb2CzPTDwSZmTgeM2MRyswcQZnZi/ufxbN7n8bB1v0n\n7Rj2t+4BAIxJHwejOpjMZBtyYpaZkdeJM9vvq1ejzdmGBlv0cTzktZLJDGVmohsAROqZIcwMlZlJ\nVKXJjT5cz8zamu9w/5Y/Sco9GIbB/w68hGMdZsnjEKOdS8DEQ7DqrLVw+OwYmjyUBp/tYZiZzLhs\nAECjrTHmfZ4M8CtgZ0pmppQpY0o297ewyUyPpztsYN5grcdL+5/vd7HAzrGLDgk7ZX4iBoBWGZ0i\nZkan1MGojuclM32/QVd1H6dBHelPIwWUaMmMXTTL6RecXogDs74WVhiGwdH2w8iPL4BWof3RW6vG\nikC/58zwZWY//l6MHyvW167BR+XLQpgZPogZ0U+DmWE/gxQzQ2KY1SdWAgAuG3IFgFPHMJB7ZrKW\nVfD82JkZf4AkM6xC4lQxWicTZ28yI2BmOAMARyscfJmZPZhIkKpa00nsoyFSmNFpY2FUG+njbDIj\nLTMjAQ4bcLGvE2e2H5YtBcDKWqK5wITrmTnafgT11joAQX23RhFkZsSUv0LGzl6JJDOzOAgzMwBA\nDD0z3GfjSxWu++ZKLCl9HUfaD4d97fGuSvxj+wN47dArkschBrnIxcyMuYM0/xcjhZud0xbBAEBs\n7X2qwQ+0osmVTiaI5CloABBd/rGPS9yB4HnAx6uHXsajOx7Enubd/To2cm1IMTMOWghgmUWSoPNl\nPy2OZpj06ZDJZFQH3tdAqqz9KM5dNgZvH34DAGDlZGbEdCTa70ZuYg7vL8nMmYA4AO/redDiaEaH\nqwPDUkZAq9T+TJmZvsnMiPrA80syc8ZwzcrL8fv1d8Ap6pnhg1oz/wR6ZoIGUeEVKOn6DCjlSnpO\nk2Hap4qZsXPF90QtK8uSKgRHQvtZxMwQAwC1QgO9Uv8LM9MfkKAnTmVAgiYRarkarY4W2L18NzM2\ncXH73ajsqmAfc5y8ZGZfy14kahJRED8Q8QJmJpuXzIQGfnwnNvI6/snQbG/C+rq19O/uKEG2uGfG\n5XNhwedzcM+GO7m/2f1peIuYuPKdokuFXCaPIjNjL6YcQw77vhLJjDOEmQm9iUk123d7iKtI7BcH\nYVvENtjlHeUAgKLkYiRrkyGDLKybGX96bZfr9M2h4Sczp3PODGVmVHqo5NGZGY/fg1LLIfp3uASd\nNCR299MOnfSkRGVmVEJmhgSXASYAi6OV2o6qiD14Hw02muwsM1rHFQasHPtHmJlojIvjF2bmjEKc\nqPfVWZEUw4pThkGr1MHpc8Lj9+CCT2bglQMv9vs4z1bwv6++zZlxUPXBLzKzMw8GjEChwcfJMCM6\nW0Bn+Un0BivkCmRxioyRqSXIi8+HRqGB/VQxM1wxK4nrre6LIQzfvOjMMzPsuiCXyWBUx//CzPQH\npAqfoEmATCZDqs7EycxCDQAqOo/RDLzZdnKSmQ5XO6p7TmBM2jjIuB+UIMuQA61SiwRNIiyRDACU\nespe8C0BPzZ/hAAToD0n0QJsKyd9IVWFA637YPfa6Ocnzhn8RUxc+dYr9UjSJMUkM8vikhkpR44g\nMyNtANAlwQARmtfuteOyFRfhyV2PSx5P8LgiMzNDk4dCKVciSZsU1s2MP7228K08bKhdF3WfJwOC\nZOY03uiDUi2WmYlWcT3SVsoOIOMo+3AJek1PNYD+V7aCzEz44J8/o4n9P8fMcMlCu7MdfsZPiwmq\nfs5xIowKWVdI4YBIW6NV2H7pmTmzEMsL+yozI8nMsJQR0Cg0cPvdaLY34YBlPzbUro3y6h8v+DKz\nPs2Z4eZZqeXqX2RmZwhiSbeUxPynxMwQJiROgpkBgr2/c/LnAgCMauMplJmx94Ekwsz0KZlh45zB\niUPQ6mg5pc5r0UCYGTnk3Pf2SzLTZxBJENHsmfRpnAFA8AcmVfgyXuNmk/3k9EUQidmY9HEAgHhu\nCBPAMjMAkKZLi9wzo9JDKVdCr4yjQRLDMFhevgwahQaXDbkSANAZQfoFBAMsh9eOABPAruad7ONc\nkO7yOaFRaCCTyehrxDbOOqUOydoUdETwL7c4WqGQKZCkTYJWoZUemukTWjOHkzFJyb0Iu9TmtGBr\nw2Z8Yv5I8ngAthJPEjAxg1XeUQatQou8+AIAbNO2xdEKf8AvkIkQ6RDBW4dfj7jPkwWBm9lpNQBg\nf7c4lR4qBTtnJpJlLWn+n5zF2luLz2mGYU5aMkNeL8Xc8QsBQNChj7CB5NgIM0OsOfuqAye9eaTY\nIDYAiFZhI5/j5+J+BYQGT2cS4gA6loDaF/CFXI9HOVns8JTh0Cl1cPmctHhypmc+nEr0e86Mz/mj\nnWf1U4G4yCcpM6OFnx8/g0aZmQjJTEHCQADA3Lz5AFiVz6lKEBw0meGYmT7IVIkCZVz6OQCAmu7q\nk3NwfQDD9cYq5ArEq+NprFnVfRyrT3xzVt0DCM7aZIY0e5MkwqQzweV3CfpkHD4HnD6nwIXmZM2e\n2U+SmTQumeG5mRGHhzR9Otpd7SESF35lnLyWZLZ7W3ajousYLiy4CPkJbBAejZkhiR0DBg6fA7ua\n2GSGBOmd7k4kchZ6BOJgUavUIkmbjE53p2QTd5vTglSdCXKZHBqlVrJfwOUXyszCWf+GY0iAoMyI\nJDu11pqIbFGXu5ParfIXbX/Aj4pOM4YkFUEhZ/uBhiYPQ6e7E9d+cznGvFeMHu4csnqsiFMZ8Pqc\nt5GqM2FT3QaBkcSpAr8CdjqrYXZeQkAaJCNZ1u5rYftl5hUsABCazLQ6W2mCJGUP3ttj48+n4EPM\nzAStmdnHg05mbDJDrc/7KJ0g+yMVO3KdmqIMjiUgn+Pnwsw8vPFhTFhWctpnNkmBMJ7ESjsWZuav\nm+/FucvGCBjLo+1HoFfqkccZALj8LmoFL7WW/RTQ354Zh89O56n19Rr8Bf2D2PRGygCA9HOYe2G+\nc7bCTpOZ8DIzAPjT+Pvw9rxltCBtUJ1KZobIzPrHzOiVcRiaPAzAmZWaEZZWJpPDqI6HJ+DB/M/O\nx7nLxmDR6muxijNWOJtw9iYzYZgZAKjuYbX7mXFZAFh3LlJV0yq02N28C/dt/mO/g1Xi7jQ6bSwA\nCHpmCANCHM3ENzu+mxQAGFVBmu7D8mUAgGuG3oBELinqjNKHwKf4bB4rtRi2eawIMAF0uDqQrE0R\nvObl2a8jVZdK5XFapQ7J2mQEmEBIJYfAwiUzACI6+rhEc2bCVeSkmBnCGPG/s0OWg2G3BYJOZgCb\n4JKKQI21Gi6/C0XJQ+nzs/MuAABsrFuPdlc7tjRsBsB+T0maJCwccjluKF4El9+FjXXrJfd5ssDX\nJp8JZkan1EHJGT9Eqprub90Lozqex8wIZWa1HCsDBJPRvoIwIFLBsLgQQHtm/GJmJgNA0Jqzr1Vh\nsj9SsSM3u6DMLDZmxu13n1Rb+LMVuxp2oaanWmAYcSZBAnByvsRidrGuZg0abPW0SOT1e1HRacbQ\n5GIo5AqukBNMZtqdbWdlJfJkQCAz62XPjNfvhS/go/PUfgoV/x8j2kR9sFI9M0VJQzEytQTfVn+D\nOmvt6Ti0Uway7sYppZmZbGMOFgy8mP5tVBth99pOyTpNmKIgM9M3A4BUXSotcp9JEwC+zIzEhPta\n9mBc+ngAwHfVoQPMzzTO2mSGVNUJC0IaconcheghO1wdKOs4iqy4bBQkDITDZ8dbh9/AcvMHfd43\nwzDY17oXOYZcKmdRyJQh20k5mvGHFgIsM9Pj7oHH78GXlZ8hKy4b03NmUjZFKrkgx8I3DzjQup8m\nPwwYdLu70O3uQopOmMzMzJ2Fo7dUYXjKCADsAkcSnnBMiMPrgN1roy5OGqVWumfGJ5wzQ26C/EVC\nkpnxhM4ZOWg5EHZbQOjw4WeC09nL20m/TDF9/rwBswWvJQlLj6ebusrNLWAp53U130vu82TBe4Zk\nZtQJUBVH+2CkKtZdrk5UdlVgdNpYZMSxCUKrU3g+1/CTGe73YxgGq098gyWlr+PF/c/h6d3/hpkz\nZIiEWJmZOBWZMyNkZsi1RgoJQWamb4EUuVZttGeGMwAgyUxUZiZ4Hv8c2BnyGX9o2nGGj4QFSWIJ\nkxeNXWh3tqORM30g7H9lVwW8AS+KU9hZFFqFFgwYuoZ5A94fhWZ8Z+N2nLN0FCo7K2J+TX/mzDhp\n0U73s++ZeWT7g5i5fPJJG97bG4hNb6SYGZlMht+MWowAE8BbpW/0e7+H20pP+VwyKZA4IJLMTAyD\nysCqW06BKoMyM320Zg4wgWAyw8nmzyQzE+DJzP4y4QE8O/NFHLipDN9cthYmXRrW1aw564p3Z1Uy\nwzAMHULZ7RYzM2yQTbJV4iNe1VWJZnsThqUMRyoXgACgQVxf0GCrR5vTQlkZABiSVIgxaWPxzyn/\npo+Z6ODMZsHr6YwcblExqIzwBDxosNXD6unBxMxzoZAraLNYZwSZmdPnFNxw1teuARCUVZAKC7mI\nxCBSMK1SR7fpCJPMkBt3kJnRSDMzXDO2uGeG36cjlcyEo3kPRUhmxAwPqZYGm/+DyUy6Ph0lpjGQ\nQQa9Uo+NdevAMAysHis1YigxjYFRHY+tHGtzKsEPhANMoE8yjr6AP6tFGYW5OGBh5zKNTRuHRE0S\nVHJViKmFIJnhfuMfmnZg0eprcf+WP+GfOx7C07v/jcd2/iPqsUXvmRExMwqhmxllZrjES02tmfv2\n3ZJkhPbMcNrgZE0ylHJlxJ4ZhhHeGH8Odr4k+TtrkhkuiSVBTTSZ2eG2oGsfSVAIsz+MS2ZIYtTC\nkyy3hZlfdbbhoW33o6anGn/dfG/MrxG4mfUyGRHMs/qZy8xWnfgaR9sPx5T0nuwgUCwz00n0zADA\npUOuQKrOhKVl7/ZLvWLz2rDg89l4aNt99LHTyV6Scy+SAYAYpKB5KvpmqAEAlZn1rnjZaGuAJ+DB\ngPg85CXkAwg6iJ4J+JkgM1OQMBA3DFuETEMW5DI5zs+bA4uzNWLcdiZwViUzH5Yvxah3ClHeUUbZ\nCtIzQ1gQwkrkGNhZKNsatwAAilOGC378/mj793MN0URrCbBB03dXbMT/lfyOPhYcnCmsTgRlPoSZ\nYT9DIzcgM55jmwgzI7Yc5kO8OK6vY524aJMYF2iKZWYEhD3RKjRI5tibcCYAJPkgDJhGoZWcYkso\nVLGbGT9RkTQACCNTinRRkIWaSPJoMtPJzZjhJTMA8NL5r+HDiz7FzNzzUdNTjbKOo/AzfrqQKeVK\nTMqcjOqeE2iw1kvu92RAnECcLnaGP7Q1yMyErxiSfpmx6eOpa2Cbsw3/2f0EFq+5FYAwmbFzvx+p\nGt0y4jYsu/BjJGuTozIzASZAg3/pOTPCoZkqhQoKmYJ+JtIzF3Qz69/QzFCZGXu9GdVGaBSR5414\nAh5BL5KT95karPUCieRPBeT72N2867Ql55FAklhSOIrGDpTykhnCeB/vqgQADEkqAsCufQDQbA8W\nqdocZ38yQ64J8nligV8wNLN3v6eDJwlV/4wNABxeBy2yRksQHF4HMv6XiPu3/Nt7N5AAACAASURB\nVOmk7b/NISwckgJQOGgUGtw8/FZ0u7uimu9EQqujBU6fE1VdxwEAt323CFd8dclpS2j46oNYQQqa\nUn0zHa72Po9uICNDDGoDVHJVr1n6E1zsOjBhEAwqA0y6tDPKzPANAMSYPYCV8689DeqW3uCsSmZ2\nNm0HAwaVnRXo9nQjTmWAkpsjYeKxLkBQZratgU1mhqUMx8LBl9PnyTyTvqCeC3IHJQyOuB3R7YfI\nzHwOqOVqeuyk36bBxr4vCaxJFh+JmREPK6rtqUa8OgHj0ycAAGp6agAAKRLMDKlcaJU6JGukmRkL\nYWY4BkyrjNAzQ5kZ4ZwZfgJ5pK0Ubx1+I2RxD1cVqe45ISm1IxT6oET2tyDyw/KOcuiVeuRyAz4J\nipKHYtaAOZiZOwsA8PXxLwEARlWw52lK9nQA6Dc7wzAMXtj3LLY3bA37vLjp/3SZABBmkDUAYM9B\nqaorSdzHckYXiZpEdLu78NXxL/B5xSfwB/xhZWbknJ894ALMyZ+HwqShqLXWREzYHD4HGLA3O+k5\nM8JCAMCeu7RnxtkCGWSUQSTJzKUrFmBF5eeS+5ZC0Ngg6GYmgwwGtTEiOwkEh5OK38vr92LOpzNw\n23c39fp4znaQz2j32gQuknavHZ9XfHLa5VgkiY1VZna4LdifR9h/4oCZzVnSE9MJPuMurn6fjSBr\na6O9ga6T0cA3b+ntnJmgnFoHlVz9s+2Zqeg003UtWtX/eDebaC4pPXmOmrEaABAsGnErVHIV3ix9\ntc/JB5lXR5Q0G+vWY0vDJqocOdUgKpDeMDOksGuTSGaGvlWAwrfy+nQ8/F5PjUJaoi8Fkszkcw5s\n+QkFqLfW9YvtrLPW0t7v3oIUOWSQhTw3M3cWFDIF1tX+ksxIoqLzGAC2et/j7kYCzw6ZMAYERGZ2\nrJN15ihOGY6/THgA/5v9JoDIfSjRQNgDknRIQapnhnjvE5CKAGVmuOQmQZsY9VhJcMB/v3MyJlD5\nXXRmhiQz2sgyMwdhZoIGAL6AL2xw4KbMDPu5KDPDG07JgMF9m/+IOZ9MF1Q7xIwZCUqlTABIMjOQ\nSyy73F3wBXyo7DyGwqQiKrcTgyQzK4+vACB0o5uaPQ1AkNXrK8yd5Xhs58NYuOLCsM+LZ8ucNmbG\nG7QG13CzBbrDBDcMw2Bvyx5kG3KobCtBk4hudzc9R7o9XajprqaGG+Rm3cJVrcnrBicOQYAJ0EU5\nHPiJrVTlyiliZth/B23CW+zNSNGl0iRNxcnMAGD1iW8k9x3tmFx+F3wBH5UkymVylpmJkpzxQY7x\ngGUf2pwW7GnZ9ZOT3pBzCwhKzVodrRj8Zg4Wr7kVH5S936v3q+qqxOI1t/Y5CSLXWNAAIJrMrJT+\nm+yTJDOZcZkAgsEgn5kJN4z3ZKLUchDXrLwsorNjNJBiGQAMXpKL76pXR30NP4EJMIFeSaCCcuo4\nzgL+zDN1ZwJlHUfpv8UjEcSw99MNMhzEKggpa2aCdH06Fg6+HMc6zTEb4bj9brxzeAlNkolducXZ\nCqunh/Yavrj/ud4efp9AZWYSQzPDIZLMjJ/U9YVx5pvuaJXaqMYxYlR1swwXiXPy4wvgZ/yot9X1\n+lgIFq+5FXM/Ow8/NO2E2++OeG8WI8AEIINMMO6DIF6TgImZk7CvZa+kAudM4KxJZhiGQWUX27jY\n4+5Gt6ebBuxAsGcGYANtEgQDbHV2cOIQaBQaTMycBCA0eGuxN8c8uJAE3IR5kEIwmRHKzBw+h6Cy\nTJIXwvgQh7E4JdugHcnNjDAzOfE59LEJGefSC7OGoyKTddFkZjq6TbgbpsXJfgaSzJAgOFwAHsrM\nsBd/OGlfZVcFvuLYESB05sukrCkApE0ASNVpYOIgAGziV2+tgyfgweCkwrCvAdjKRn58Acyd5dyx\nBhPT4akjkahJpKxeX8GvTIvx6bHluGPtbQCC1Y3TlcwEgww9vR7Whqmi1Nvq0Oa0YCznUAKwPWoM\nGJqgN9ub0WRvxMCEQdAr9fRGQM55wk6S34IUJMKBP31ZSmbGT8QItAodr2emle4TAFTyoDFHJLmm\nFPhJlc1jhdXTQ69XvUofcWK0uO+HDALdWs8yfm6/GxVd0t/HjxFOnxN6zkHoB84i/tsT39BKnsXR\nOxvjS1dchM8rPsHrh/7Xp+Mh5xGR8kYKRBxeB73HAEGWt8nWBIPKSIszWm7ta3HwemZOsT3zO0fe\nwvratdjRuL1Prw8wATTZGmFQGSnjv6rq66ivEzes9yaQ4wdwKrmqzyYcP3aUtQeTmWjMTCQ3SF/A\n17f5JE5xz0xkZgYAbh91BwDg9UOvwB/wh2Vovj6+AiPfKUR19wm8sO9Z/GXzPXhu3zOCffoCPhxp\nO0xfs71xK/a27O71Z+gt+mYAIC0z4xeU+e6dsYI/90bbD2aGxDknw9GMuN7+bt3teGHfs5j8wTiB\nyiISAkwgrMSMYHbeXDBgsLHu9AwgjwVnTTJjcVroCdXt6WaZGa5PAgCSNMk0iOGzDACrdSaNwCQB\n4tPsjbYGTFhWgoe23R/TsZBqbVyUZCZZmwyFTBGWmeEvKEbKzLDJDAmWZDIZEjVJEYMwsmjkJ+bT\nxyZknksTInKySzEzpK9Fp9RGlJnRnhlOzqehLlKhiyvpmSEBJ6nuiReJByY+BADY2rCJPiYODidl\nTgYAlEokM8R2siiJ7Y2psVbTYyXJpBQIOwMIWTa5TI5JWVNRZ62N+eIOB6kEzO13C841ch7Fmkz3\nF/wgY27BfChkCqyq+ipkO9IvQ2YpARBccwDbMM2AQV58Pjt0jPv9WhzNkEGGFF0qAGAwJwM83iXt\npMRnZsLJzNbWfIdSTgYklJlp4fI7YffYYfNaaa8aIGRm+qJ35ve52Lw29Hi66fWZoktFp7szrEPR\n4bZSTP3oHMFjhJnhyxfPtibJ/sLpdWJw0hCk6dPxQ/MOMAxDCyFA0A0uVhBWhEwn7y3IOUV66iI1\nsR9tP4wAE6CmIcT+v9neSFkZIMjM8KuOp5qZ2cMFHn1lZtqcbfAEPJiRex6O3lIFozqeDleOBPH8\nqb4lM2xvXrThvD9VlAuYmcg9M5Ek5Y/seBDnLB3V60BYPNRVypqZj5K0MZiQcS7W1a7B5A/H4cZV\nV4ds803VCrQ4mvFm6at45cCLAICvKr8AwzCCfR6wsDP5SGHypf3P9+r4+wJqzdwnA4DQZIZ/rVdE\nuIdJgX/P1Sg1UYcti3Gi+ziM6nikcHHcyXA0G5w4BACbnH1XvRp+xh9zP12A8UMeIT0Yz/Vs8xP5\nM42zJpmp5FV0m2yNYMAImBmZTIa5+aycp8vdhWReMlPMDRkC2OxbLpPTRnEAWF+7Fk6fE58cWx5T\n5YOc7CQRkIJCrkCqzhSmZ8YpqBgQE4MGTmZm5M2sSdImRbyBNXGOOkNT2HkqSrkSY9LG0fcIysxi\n6JmJYABg4SrtqSHMTJhkhmNmtApSkeN6Zrjv7cFJj+LzX63E3WP/iHR9BrY2bKE3OXHlqjhlOBI1\nidLMjLMNcSoDZuTOhEquwprq7+hCSgJpKczMPZ/+WywZpFKzfrAzJFAVf/dfH/9SsDiSfZ82Zobn\nCJasTcHk7GnY27KHyhwJ9nF62nE8ZiZRlMyQzzggPg9GtTEoM3M0I1VnonIvsnDymRm3342Vx7/C\notXX4fIVF9O+LICVA/ElWFZPD25cdQ02cJUePa8YoFXq4PS50GwTDswEglOtgfBJejTw2RWrxypw\nvkvRpiLABMIyp8/seZJKcsi14vQ50eZsw67mnbSYwXfPOhvg8Xv63OTKMAycPie0Ci0mZk5Cs70J\ntdYaunYAvU9mCNR9dJ8kFVqShEeSOhGJGZmn1OPpgdPnRKe7ExmGLLodX6ZDWNVTKafodnehnHNn\n7HD3LZkhhbJsQzYUcgXGp5+D412VUY9bnLz0ZtaMgze4UKVQgwETcTjvTxV8mZlUPwZBpHv93ubd\naHE0hzT0R0Kbsw3lHUcF96BoPTMEhJ050V2F72u+DXm+1MKuXa8f+h/sXhtStCmotdZgX+seQXJ/\noJV1xLyi8GqMSRuLVVVf98oevC+gQzMjzJkRgxTBark+Yz74zGtfjj1ouhMHvTIO3e6usHFWOASY\nAKq7T2BgwiAq6zoZzAw/sSb3IUuMVtoBJiAp4QeAQeR+fxYpD86aZIb/pRC74XhezwwAzONmhABs\ntZs0/w5LHUEfl8lkSFAnCG6qm+o2AGADpnUxNKiRG2Q0mRnABlYtjmYa2LA3fDEzIzQA4A/gTNWZ\n0OnqlKyINXOVy6JU1mlnZOoo6FV6GiCTxsOobmZKbXBIZ1hmpo0eDxCkqsNVGEjPjFaphVKupDdA\nspDnx+djavZ0yGQyTM2ejjanhcq9xFI0g8qAkabRqOo+HrZptd3VhhRu+OfU7OkobTtIA+xUbeRk\nZmr2NCi4oZH8BBLovwkAwzCSfT5vH35T8Df5rU6fAYADKrkKKgV7fZDBYWLZyb7WPZDL5BhpKqGP\niZkZcqPKi8+HQW2kv1+LvYX2ywDAgPh86JVxdJjiktLXMPKdIfj1dzdg9YmV2NKwKWS2D3/WTKuj\nRRAICZgZBdsz02RjE3u+zEx5EmVm5Bj4zAwQvirPr34SV0KXz4nn9/4Hbr8bfxz/V8hlcoF71tmA\np3f/G+OXjupTj4o34EWACUCr1GFixrkA2L4ZfpIarjdLCnymUmx0EitIMhoLM0N+iylcIcPq7kEz\nVyziMzMa3m+bG58HGWQ40l5K1+jXD76CP6z/3UljIfa27KHreF8TTVIoy+JMDM7JmAggKDWRwslh\nZnRU7vlzczTrcHbQcwiIzsxESlZJUbSrF/2+nx/7GL6ADzcPv5U+Fq1nhuDCgRcLCsH8YpvdaxdI\nMgfE5+M/M18AAHxZ+blgTTzI2ftnxWXhzjF/AAMGrxx4IebP0Bc4fHZoFdqIUigxRnMKBKJI4IO/\nhkVSFwCswcvTu/8tKA6SxF6n0uGKwqvh9rvxxA+PxXRc3e4uuPwuZBmy6WP58awRQH+YGb6Um8Sn\nlhjlsgGGgVwm/d2m6lKRqElERacZbx1+A1+Wfyk5boFgf8te3L/lT6dMoRJzMlNUVCQvKip6taio\naEdRUdHGoqKiwaLn7ykqKjrCPbexqKioqDcHwmdmSNMTn5kBgKnZMwAABQkDIZPJqNRsGO+CBFj9\nNLmpBpgAtjRshJ4Ljr4JI7cRw87TP0bDkKQhcPqclCEhdq16XjBGglmSYBl5zegmXRoYMJIyBsLM\nTB0wFTqlDvMLLmLfQyVkGqR6ZvLi8wGwVtZKuRIJmkRJa+YETSKV61FmJswkW5ffCbVcDblMDoVM\nSauhNuowEkwCp+Wwv9nW+k2CbQj0qjiMSmWDaX5zLsAmDO3ONpi4oHJuAcvMfVS+DABCBoWKEa9J\noBbWYmZmaHIxUnWp2MZjjXqD94++Q39Pq8dK36O07RB2N/9Adev8fZ8uZsbutQno9wUFF0MGGVby\nzn1fwIdDlgMYmjxMkLSLmRlS0cmLz4dBZYDDZ0ePuxsOn50OlAXYpGJ67kwc76pEVfdxvLz/Bbj9\nbtxRchedzSTu2+EvfpYIum+dUgc/40ddN7su8GVmap7MrNvd3euhdXyZGZE8kWQmlTu/2p1t+LLi\nM/zmu5tpAEe2BYKuV8c6zXj78JsYYMzD/5X8DtmGnDM6xTkczJ3l6PF0o9HWGH1jEVw0eNXSXqwf\nmnbC4milDEZvkhL+d9PXZIYyM+roPTOH2w5CJVdRJ8geDz+ZCTIz/Dkd6fp0XF54Fco7yvDawVfA\nMAxe3P8cPih/X9Bw3x/w5WB9lZk1WNlrI5sLiCZkssnmLq6vye61h/1uiJsZqcL2pomfMjNKPWVI\nf259M0da2b7JQs7WO1rPTIeEXTs7Y49dR3pjXrTc/CGUciVuGv5r+liszIxSrsTGq3dg4eDLAAgt\nno+2HwYDhhov/W3iQ5iTNxeJmkR8VfmFgMkg0qWMuCxcWHAxBiYMwsfmD2H19MAX8MU0TBlgr93F\na36NhV9eGPWebPfaBX2VsSBNn4YB8fnY27I75P358VdllGRme+NWPL3733hiVzBZoYm9QodbR96O\nIYmFeO/o27SAwjCM5L2JxKr8e2+qLhVxKkM/mZnQczFWZsbP+CMyMzKZDIMTC3G8qxL3bf4jLl1+\nKYrfLsCi1ddhefkHYRnKh7Y/gCWlr/e7V1kKvWFmFgLQms3mSQDuA/CM6PlxAG4ym80zuf/MvTkQ\n/glUzy3M8aJkRqvU4sBNZVh9OStFIfrCYSkjBNsRRyaAdYnpcHXgokG/ggyyEKlNONg8Vm7goDLq\ntiNoIM6etMSuVacKNQAg4CciwVk1QqkaQZOtEQqZAsNNw3Hk5krcNeYe9j1476lRaBAnQbdOypqC\n0psraHU+WZssYc3cSpv/2fdkb+hhZWY+N+2p0SjUaLY3odPVQXtm+InDVI4B2dKwGQzDhOhV41Rx\nKDGNBhDag2L19MAb8CKFY2AmZrABFGHuosnMAGBewQIACLFwlsvkmJw1DU32RpzgnERixffVq/GX\nzfcgRZuCAfH58Aa8NFF5m5usfM/4P9PtCcN4KiYPh4PF0Spw/0uPy8D4jAnY2bSdNmibO8rh9Dmp\nJTOBmJkh1Z28+AKa9JBmRT5DAgBz8uYCAL6p+hoNtnqMSRuHR6Y8juuKb4RcJqevI4EPn5kRSyv4\n1w+pNJ7oOkE/DwF/OC4DptcyJ35C1cQF+Ebu9yLnXburDe8ffQcrjn+OZ/c8BSAo78wx5GJxyZ0A\ngFcPvgxPwIO/TvgbNAoNkrTJ/XJV7C1isfIkbkq9YVAInNxaoFXoMDx1JPTKOOxq2gGLsxUpuhQk\naZKiWgL7Aj5UdVWiydaIQ23B693q7msyY4dSrqSFJylmwBfwoaz9KIYmD6PrRrenO+hkxpOZaQSs\nWyIem/oEUnUmPLnrMWyoW0uDznAV3r5gd/Mu+u9IZjCRQJgZYi89Nn08FDIFdjXvhNPnxMRlo8P2\njJJ+RzKbxN+bZMYXNOsgw3k9PxJm5qX9z9OiWH9Q2soW4EiCHM3NjPTMiAPFLncnvYfEysx0uTpR\n2nYQk7KmIsuQTddCXQw9MwQymQzpHCvJ730jQfiDkx7Bjuv24tIhV0CtUOPCgovRZG/EjsZtIe+V\nZciCQq7AnLy58AQ8ONZpxgdl72PaRxOiWgQzDIM/b/oDPq/4FNsbt4YMIhfD4XVE7WkOh/Hp49Hp\n7gy55/OTs2iuX6TgsKpqJf3N+EyRWqHG49OeQoAJ4G9b/gKGYfBl5WcY8HoaqsL0rNCZiqK2ivz4\nAtT0nOhTsdXj98AX8IXEPfzfOBKiGQAAwOCkIfTft4y+BTmGXKw+sRJ3rV+MBZ9fIGjpONZhpu6X\nZBzEyUZvkpmpAL4FALPZvBPAeNHz4wDcX1RUtLWoqCi2TnseKroqYNKlQSlX0hMkQZ0Ysl2WIZtK\nqmbkzsLkrKnI4EkEADYgc/js8Pq92FTPSsxmDZiNBE1CTJUvm9cW84UyInUkgGAyE5yKHKyO8Kt+\ngNAmmOj/a621AsaEYRgcbT+CFkcz0vUZUMgVMKiN9ATjJwzpcZlhLfTo8/p0+nyyNhmdrg7BBeIP\n+NHubBc4xGmVLDMT1gDA76Qym2uLb0Sb04IbVl1NLxS+c9iA+DwMiM/H9satsHttIdafcao4jEoj\nycx+wXNk6jYJPogdN0FKFJkZANxRcifWXbkFo9PGhjxH5CZbe1Ep2N+yF7d/fws0Cg2WLviYJmJW\njxXd7i58VvExBhjzcOOwW+hrcrgFhW/12l9IVXm8fi/aXe0h5ggXDfwVAkwA31az9sVNdjb4Idpc\nAjEzA7A64FRdKu0hI7MS0uOE+yDDtN49vAQMGNrEaFTHY2RqUMpGTCb4iYR4VgK/F4boos3tbH2E\n/9lUvO2A3le2+cxMo50kM+z5S2YuWZwW1FhZnfXLB57Hwdb9aLQ1YFLWFOy76Qg9j2xeK4qTh+Oy\nIVcCYOVnDp/jtDByh9tKcc7SUXj78BsRtyPFhO4+BM2EmSHy0vEZE2DuLEe9tQ4mXRrLiEdJJv+8\n6Q8494OxKHlvKH679jf08Wivk4Lda0ecykCLQlJzLiq7KuDyuzAidSTUCjW0Ci2s7m4qXeSv0XyZ\nTrw6AcnaFDw5/Vm4/C7c+t0i+tyek+Da5Av4sK9lDwYmsC5GUufv5xWf4GDrfrx1+A28FeY3bhQl\nMwaVAcNTR+Jg634c6yhHq6MlrCzWHyB9X+x11JtZM/zZGmoFGc7740hmHt3xIH6//o5+v8/hVtbJ\na3wGm8xEZWY4mZk4vmjhFTPFBZBGWwNmfTw1JHnexwWF53DqAwOVlMfGzBCQwhe/oEqSj1Gm0bQ/\nAgAWDmFn+REpM4FGoaFyW7J9ZWcFjnHycjJCQwpP7f4XlpW9R2Xhj+18GOcuG4OZyyeHLUA7fHaB\n+iVWEKWG+NolyUycyoB2V1vEBIKsVT2ebmpt7fQ5BWqCmbmzML/gIuxs2o4vKj/Fhrp18Aa89DcL\n934JoraKXGMuHD4HOvvQR0eS6iEix9fYe2b8kIeZMcMHub8DwJuXvIlt1+3Btmv34NLBl6Os4wj+\n/cM/6fPLyt6j/z7Qui+mY+gtolMPQcQD4N9x/EVFRUqz2UxWv48AvAygB8AXRUVFF5nN5pWR3jAp\nSQ+lUgGn14m6nhrMyJ+B0pZSOjk7Py0bJpP0rJf/LXwx7ONpRjbZURn92N7C9kRcWnIRntn7BDo9\nHRHfEwCcfgfitcao2wHAeXFTgK8Bc89RmExGdMrZ/DDZkEhfn5wyGEq5ktL8A7OyaT/DoHR2SNPi\nNb9GnDoONX+ogUFtwBNbn8D969iccGI2q3/mH09cYjBrXlA4P6ZjBYCM+HTsbfFCmwDEa9jXtNha\nwIBBTlIWfZ+cFDZB9GucIe/tZTzQq3UwmYx4/uJn0O5twYeHP8QersKYn5kJU0LwNXMGnY8l+5fg\nqF2YrABAXmYGBsqzEa+Jx5GOQ4J9VbrY4Ck3mT0uE4ycYQIbiA3NLYBRE/1zZ6RPDfv4JSPn46+b\n78Xm5nX448zfR32fZlszblh9FVx+F764+gvMK5qFT46z1T2NkcHXxz6D0+fE7yb+FvlZQfZgRNZQ\n4CjQxVhi/p0iYX/Tfox9fSyWXroU14+6XvBcQw+78OcmC6+dG8dfg39sfwBr6lfh3hl3wd/EJqm5\nqZmC7fKd2RBjUPJApKXFI9XIyjqr7OyNqTBjkOC1JlMRilOLUdZWxn3uYvr8wmGX4OAm9vfPTshC\ng60eGoOMPu+UCSvzaWnBhL8wfRBwHFhbtRYAMLZgBEzx7OtM3cIbAHSeXn3HTn+wZ6bNwyabmUkm\nmExGDLKySagdXWiw1iFRm4guVxduX3szGDAoNA2GyWREhjXYfPvU3CeQkc4mhBkJJqAeUBp8MBmi\nJ979wdaj61gpnutExM/vDLABaEDj7vW5aAG75iQZ4mEyGTFr0Axsrt8At9+NrIRMdLo6Ud5WHvF9\na+xVkEGGa0ZcA6vHCrVCjc/LPoeLsYe8ztxmRlpcGpJ0SRLvBrgCDhg1Blw97jI8d2ACvqz8HHdN\n/h1mFQSdDGu6anCgi+0dmZQ/ASaTEYm6RNj9NnT42QBueM4Quv/0tuDvWZI9AiaTEb823YDVdSvw\n6dFP6XOlHfv7fT0faD4Au9eG8wZejY6ydvT4ukLec2/jXixecyumDZiGLbVs4eXSUReh0BQMVFrd\nTVDKlRieN5gWvGYUTMMhywGsbVoFAOj0tIe8t1bPbqtT69Dp7kRCoham5Bg/k4q9n2WbTDDq2YKD\nMVENU2L/17hTCX6Brr+/3+HWw5DL5JhVNA3YCAQUkdcfq48kKoxguwM9wdDKpxLecx/Z/RIOtx3C\n9auuRNtfgkWf8iNsAXVW4QyYTEbEa+PR6e5EdloqTEmxf67BXAziUlhhMhnBMAx2NG9Fsi4Z04om\nCFikS1MWwLTOBIvDgqLUIlR2VMLlcyE3IZeu2ePzS4DNQJOnFj0B9l5tl4We1wSv7XkNz+x5EoOS\nBuFPk/+EO765Ax+bP6TP/3nr7/HtDd8KjsPutWNQcjBGi/V3nD10Jv62FTjafRAm0+30cWuA/V2K\nTUOxp3GPID4SI6AKFqdW163ADedcBXfABYPGIDiOly9+AcUvr8GjOx9Eqp5d/7sCYWIACyvNzEnJ\nEDyXk5QFVAOMzgVTau/OU1c3+3kyE9KRHpeOFju7znV6Q9eAcJDJAYVCEXHboZlsp8m4zHGQy+Rs\njGYah6UF72Hg81vxWeVyvLLwBXj8HnxS8SFSdClQKVQ40LYPqamGiAX4vqA3yUwPAP4nk5NEpqio\nSAbgObPZ3M39/Q2AMQAiJjOdneyN9Ugbq8/MixuEKlU1TWbimVRYLJHdQcJBC3ZhPVxbga01WzEi\ndRRkDh0SVEk40XUCra09Eb/IHrcVA9TJMe5bg6y4bOxt2AeLxYr6NjbDl/mUgtdnGXJQ21MNnVKH\nrg4XAHZB1fnZBcDtd8PtdGOL+QcUJhXhia1P0temqNnKCf/9+JWDmRkXxPw9xcnZ/R2rr6H9NOXt\nrHzHKE+k72Ng2ADC3FQFS4rwvR0eB5I0we/n6Skvorm7lTpRuXsAC08zOT5lEpZgCZYfZAMBjUID\nt98NtVzNfRfAkMQiHLIcQEtrN120KptYOZmOiaf7yjEMQKerE2q5Gs5uBi5Z788PgmQmC6NNY/CV\n+SusOLAak7PDJz0E39R9A4vDgr9O+BsmJZ8Hi8UKZYBlsGqam/DGniXQKDS4JPcqwe+RImcTm8rW\nE4LHrZ4eGFTGXl/U/1j3KADgnm/vxQWZlwieK2tl6fN4eZJgXwakYpRpNNZVrUNlfR1qWtmkR+nV\nC88rp5DpAIDsuAGwWKxQ+NnP+umRzwAAw41jQ867ktSxNJkxKbPo87cPmvoFYQAAIABJREFU/T0S\nZKkwd5bDoDJgV8MuNFgssOjY52vahf0H/PfNULOMXKO1EXqlHiqXERY3+3xXl1C6d6K5HgM1wh46\nKQSYgCCwqe5gzzeFT8N+Xjdb+fuhZjf8jB+zcueAYQL4opL9/OnqbFgsVvgdbEA4MXMSJiROp8eu\n45bLnZX7kGXIRgE33flUYPWx7wAAjV0tEdeCbiebNNa3NdPtPH4Plpa9i6sKr6FObuHQ2MoGUoxP\nAYvFihEJQbYzQZkMj9wHh9eBxuYOWqwRo9PeDYPaiOenv0YfW3VsFdptHYLjbnG0YPz7JVgw8GK8\nOuctyWOyum1I0iShvc2Oxyc9jQs+nYk7vv4t1l+1DWqFGr6AD0VvFFF2LF9bCIvFCoPSiE5nJ8qa\n2cQ83m+i+3fbg+zxxJRp9PGHJzyBdcfXgQGDzLgs7G3ci7omS8wN1+HwXRlb2R2ZOBZr1evQZm8P\n+f0eXsdWOHc1BOVoD214CC9Mf52uHTWdtcjQZ6KjPcg0juR+n/cOsINMW2ytIe/dbWWvH5WMve5b\n2roQ749tTW3vYQMml40BaZVpsXQizkt6zdqxeM2v8ZcJD1BDgrMBfBbkRENjxHM+EhiGQWlrKQYm\nDILcxdl593REvP4sNvYacvlcgu3MjUFpU0N7s/A9vGyY1u5sxx1f3oUrCq/CKNNobD6xFQAwSDsc\nFosVegUb9zh6ArD4Yr8vav3s5z/eUgOLxYrq7hOo7a7FhQUXo70tVBp9YcElePfIEiSrTXh//uNY\nW/s9pmQFrxOTjGUHDzYepixTlaUm7Pfi9Xtx73f3IlmbjGUXfiqYOD8tewY0Cg3WVH2Ppzb8F7eO\nZJMPX8AHt98NjUwHi4VNwGKNf7IVg6BRaLCtZofgNQ1dLEM7IK4Ae7BHEB+J0djBshtymRwryr9C\nbVMrrG4bEjWJIffcm4bdgjdKX0WjlWX9zS3HQ4611sLuW+7VCF8vY4tiFQ01SGFCi4yRUNvBnuMK\nvwbZcTk0mWnqaY7pu/L6fJAx8ojbXpB5CR6b8gQuL2RtvfnbDk0ahk31G3CioRHra9eizdGGxSV3\noqanGqtPrMSharPA8CAaiKInPS1BcpveyMy2AbgQAIqKis4FwO/WjgdwuKioyMAlNrMAxCyMq+Sc\nzIYkDhH0l/Tmw/JBdP/fV69mvfdzzgPASqzYKd/S+myGYWD32qLaMvMxylSCFkczmmyNVDojNg/I\nNbABmdgDXiwHKu8ow2uHXhFQzWI5DwBBAExkLrGASPT4jYiEeuT3WRDpXrOtCWLwe2YAtgn7rXlL\nMSHjXGTEZYY4h5G+mW9PsBVC8pn5TepZhmx4A96wsx34jf5EA5qiS+13Zi+TyfDEdLb165m9T0Xd\n/lg7e54SJzQA9EbY6e7Esc5yjEgdFWJMQMwZ+HT55vqNGPb2IDy8/e+9Pu6qLjZhEUvEgKAmNtwM\nngUFF8Mb8OL76m+p3EFsKx1OZkYWdXJNVHUfR0ZcJoqShoZsOzYtqD7lB+8qhQo3DFuEf075NzX2\n4LuttDmkLWSJBId9z0GCCp1YX96bngOyf/KZifTOSA0A2GoacWjLj8/HP6c+SdeXAfFsRXN02lj8\nbeI/8OKsVwXnJDGBuGHV1Thv+ZRTZu9r89ooKxpNZkdkMPzv7RPzR7hv8x+jDq4kPTM6rr+C9GUA\nrHSQaL4jNfNbvdYQl0ijOj7kNetr1sDtd2NL/eaIkg+H107XkZK0MVg0/Nc41mnGa4deAcDKy/gy\nv+Gc82W8Oh497h5U95xAoiYRidog+8OX6YziZKQA29+48rI1+OxXK3F+3gVw+V1YWbVC8tiksKb6\nWyw7ysoudjexjNE5GRPDSoArOo9R0xr+51h+ZDnu3vBbMAwDX8CHZkdTyP2SmACQvqAeTzdbNPO7\nacMycTMj96VobmYPb/87ntnzJLXpBjg3My55dfMMAJ7f9ww21W/A4jW3hn0vPqq6KrH06LunZU4N\nvz8rWm9GJDTbm9Dl6sLQ5GH0HIzqZsZJyYkzYPA4ggmWeE1T8woDrx58CZd8MQ8BJoB9LXtQkDCQ\n3m+IdE3Xy+TaxN0ryL2DNGhPy5kedntiGJCqS8WM3PPwzyn/xjzOnAdg7z0GlRHHuypobCHVE3ys\n0wyHz0GNA/I4V0wAmDVgDv573ktI0iTh0R0PUpcxvvFEb6FWqDEytQRH2ksFMuc2pwXJ2mQqgY60\njpLYbE7eXNi9Nqyt+Z6TmYUez6wBswV/E6MO4fuFGgAAwftPrA5kfNDB2So9co3sfSpFm4J2V1tM\nJjkunytqz7hSrsTtJb8Na8SUn0Dc2KqxtOxdAMANxYsw2jQGACSdYAmWlL6OT48tp38/vvMRjH6v\nOOJrepPMfAHAVVRUtB3AfwHcU1RUdF1RUdHtHCPzAIANALYAOGI2m1fF+sZkNsXgpEJBL0ifkxlO\ne7ii8gsAwIxcLpmhc1aEJ+o/tv0NY94bBis3dyDABHo1jInoZfe07OLpiIW61Ryu30N80xYHnT80\n7cBrB19BijaFXqytEjrH/81+E2/NXSpwdIoGErzydZhEL8rvmSFN1s28KdgBJoANtetg81pDkrI4\nVRxWLFyNndftD2kcS4/LQGFSEV0sz+UGZfJ1w1mcZr2JF/CTPopUXqM/6ZuJpfk/FoxNH48UbQqa\nY3B3Ij0bgxKCRn5GFRv4Hm0/DF/Ah0GJwedWXroGi0vuxOSsqUjQJNJkps5ai9u/vxluvxtvlr4a\n1vdeCg6vAxWc/jicFSI5V8IlMxcN+hV7XFVfUQvYRI1QwsM3ACBVsnwumeH3vUzJmhY2meTPrOFr\navkgNyu+IUKkCev8ZIZMSCYQ3wB60zPj9LLBGEncybpADBtI4k+SkAHx+UjTp+HJ6c8gKy6bOnrJ\nZXLcPe6Pof1HXIBs99rg8NnxVunrMR9bb7CzcRttfI80a4cUagBhwLSFG2q7uX5jxP3we2YAzlY9\ndRQAthBC1t1uj3QDs91jDXEWjNeEJjPE+c7ibJUcbOsP+OHwOQSFo/snPogUbQr+ueMhXPrlAjy/\nN+hTo1fG0UTVqI6HJ+BBZVdFCGPGDzLFa9mQpEKMTB2FG4fdDAB45/ASyc8qhetXXYV7Nt4Jm8eK\n3S27kKRJwqDEwUjUJAkSDQB4cf9/wYARXAN/P/dhnJN1Dj4qX4Y3S19Fi50dDZBjzBHsJ8uQjRyD\nsM+w3dmGa76+DJd8MY9zWGKTF1KcanNa8MK+/4Y1cggwAbx68CU8uetx/GnT3TQx1ivj6HXIL8KR\n3y2W4Om5fc/g3o13CcwQThX45yepWPcFZL7M0ORiuqZF6plx+pyC35ZvUdvCs3cOKdCI7LodPgeq\nuo6jy90lKB5lG3JgUBnDBtWRkEZ7Zth7BxlXwC/a8TE5ayr+NfUp/H7svWGfZ52uBqOq6ziauWSx\nRSKZIUOSR5jYdUQuk6M4hWXWZw2YjfS4DDw94zk4fU7cue7/4Av4aBGqNzEaH+PSx7Nunty+nT4n\nWhwtSNWZIg4WJyAmM2QN+OzYxyHjOAgmZk6iBR8AYR0QgwYAomSG69mMdG+UQnDwexx+P+5ePDjp\nUUzKmooAE4g6j83msaLeVieIZXoLcu/f2rAJm+o2YELGuShMLqJjVMraj0i+lmEYPLrjQTy563H6\n93LzBwIL9HCIOZkxm80Bs9m82Gw2TzabzZPMZnO52Wz+wGw2v849/77ZbD7HbDZPNZvN/4j1fYEg\nMzOYx8zolDpqD9xbkAphWccRaBQaGnRQVoLXaO/wOvDe0bfRYKvHN1Vf08XIoIqdeiYU+q7mH3jM\njHBByeZuNGJff3FQvtz8AXo83bhzzD1444J3AAC/Gbk47H4vL7wKFw26JOxzUiB21tesvBxD38rH\nozseokmGiWd5m6HnmBnuBLJ5rLjl2xtw9cpLAYQmawB745eySyTsTKImEVcWXQNAuBhlcolrI8/y\nlgSR/EZ/ysxIzNXpCwxqI6xhpgKLcaz9GBI0iYLkigRmB1tZZyZ+0DEhcyIenfIvyGVyZMVlo9He\nCKfPiV9/eyM6XB24IG8evAEvZi6fjNcOvhzTsW5t2ARPgL0JNthCqzykAsa3LyYYklSIwqQibKhd\nSxdVMTOjVWrpdUeKCYSZmVdwIa4vvgkAsGBg+POuOGU4dEodkrXJIW6EBOQ3PMRzr2tzWiQrbRlx\nmfQ5fiIJsNW7V2a/gdc4KdIrB17EN1Vfx1ThJYUHseML+U3VCrUguSNMzGVDrsSBRWWSMgSCJFGi\n+PbhN6J68fcFZI4WEDmZc/gcNFAnwSrDMDR42dO8K+LxEUken7kg1f80fRq1nI/kaGb1hDIzCeoE\nAVvu9XtpYy3AFonCwRkmqEnSJuOhSawsa1vjFnxW8TEA4F9Tn8KaKzfR7fjnvTjpnpo9HX855wHs\nvD60x4+gIGEgzss9H7uad+JYR+zGnfzz8vuab1HbU43xGWxfAlmbyW9Yb63Dp8eWY0hiIX4zKtis\nPiFzElZcswKpulQ8sv1BfFvN1g3JjBk+JmQK5V0WRyv2te7BQct+HO+qhC8gZGbeP/o2Htv5D/x9\n230h72X19NDz5/2j72D1CVZFrlPpaCGM75ZUzgX7sRTbiFX4epF9e2+t1mMB38mvP8wMmX5enDKc\nvfcp9WGZmZqeaty9/rfY2bhd8Dh/7lgkAwBx8KmUK+k1MT7jHPr4f2Y+h3VXbYnJhZWPFF0qZJDB\n4myl60GqzhSWeQfYZOW2UYsxLGW45HsOTiqEJ+Chn4Xcl9x+N149+BJ1JCXGSaQoAgB/Pud+/HXC\n3zA0ma3EXzL4Ulw25ErsbdmDF/Y9iy3cmIdYRmeEAzEB+KFxO+xeO25YdTW63V2YmDk55BoMhx6O\n2ZucNRWDEgdj1YmvEWACGGkaFbKtQW3E6LQx9O/6cMkMl1yLDQDINdXmtKC2pwYv7Hs25hktDt54\nkZGpo3DXmD/ApA+9RsPhKHdeDxe5BPcGpLD3wr5nwYDBDcNY4xQy16isQzqZIaRCq6MFDMPgSPth\nSWaPj7NiaGZFZwW0Ci1yjLm0Wi+WKvUGpPIPsD8ICbzJDYyfzHxXvYpWKz85tpy6/fQm6y8xjWEX\nmOYfBA4vfIhtbAnCLTypOhNuHnEr5uTPQ9Pizqi9HL0Bf7hmh6sDS0pfQ4O1nu6XQK/SI0GTiBZ7\nM6q6j2P+Z+fTmxcAoJcSrxm5bEPu1UXX0d+Bn/gQZoYvxQrKzHjJDBdQnixmBmAr8dYok5t9AR8q\nOyoxOHGwgJEggS8JzPnJDB9ZhixYPT24a91iHLTsx7VDb8C78z/EXWPugZ/x0RkW0fDaQVY+k6pL\nRZe7K8TPnVz0fMkgHxcNvAQuvwvfVa8GAIG8hiBBkwi9Uk/nyAww5tPnnp35IjZdvZNafYuhlCvx\nxLRn8Cg3WyYcpmRPQ7w6ASurvqKfuc1pQY4xF3eU3IX/znxJsL1MJqO0tZiZkclkuKLwagzmXFsa\nbPW45dvrsXDFhVEHVpIKX2ZctqB6xpe68pPmaMmLGPzvVgYZ2l3tgsbWk4VN9RugU+pQnDws4k2Y\nXzUm1d/Krgp6zngCHjpkscvVGXLjFDMzAHB98SJMyZqGGTnnBZkZiWTG7XfDE/DAILaqV8fD7XfT\nZGl38w+wenqoJGGPRLXeLlGhvXrodXh1zhLBmntV0bUCZ59zs6bQf4sZNblMjj+dc5/ktczfDwB8\nXvlJxO344DPir3PXMimG0fsTt83/DrwIX8CHu8beIzj2gQmDkGnMxAuz/gdPwIMHucQjO4ySQdyr\nUt5RRtmBNTXfUfcywups5yx3PypfRoNGeuwcQzCvYAHGp0+giY1OoaPrDZHEtDhaqMVtva0uqnzN\nwp2D62rX0sfMHeXIfd2Eb0TDfvsLfrLdH4dJkqyRAC1OZQgZPbCvZQ/mf3Y+Pixfisd2Pix4ji/J\na7DVQSlXQiVXhTAz4rlwcSoD9nLOZiQwB9h1uy99eUq5Eim6VLQ6WnC8qxItjmZJ5j1WDBXN/iMM\n2CPb/46Htj2AF/f9FwArN5JBJhivMWvAbPxx/F8F+39i2n+QEZeJJ3Y9ht+tY3tnYnEzDYdpOTOh\nVWjx3tG3cd03V2BL/UbMK1iAf017KqhciSQz83RDIVMgTmXArzjJHQBcN/TGsNtfNHAhNAoNipNZ\n9Y84WSV/h8rM2His3dmGezbehcd2PoyX9z8f02fkMzME9BqN4mh2pJ3tIBnOOfX2BaRA1OZsg1Ed\nj4sHLQTAFg4NKiOORmBmCEPo9Dlh9fTQXuxoOOPJTIAJ4HhXBQYlDoFcJqe6YLEUoTcYnjqCNotd\nwjvZSCDfzusX+cT8EQBggDEPW+s30SFF4uphJOhVeoxKLcHelj24Y+1tAEKZi3C9CGKQSsTvx95D\nT8LeTLiNBfyKZK5xAJw+J74+zuq+SeZOkKHPQFnHUcz99DyYO8tx+6g7cAXX7LWjcWuv9jsv/0Is\nmfse7pv4IE30+DIzwsw08eRe7a7QZGZocjHkMnm/KFAxjGoj7F5bxCpgrbUG3oAXA0XMADlPKjh2\nURxsExCW46vjX2C0aQyenP4sFHIFHpz0CM4fcAHqbXU40RPZ335/y15sadiE6TnnYX4Bm0w0iGwr\nI8nMALaqS6BT6sIybKNNY1CSNgYFCYOQoElEbnyQuZDJZChOGRbxRndt8Q24quhayefVCjXm5s9H\ng60e+1v3wh/wo8PVgRRdKh6Z8jiuH3ZTyGtIYCn+/gmIPABgLaJ3NG7D7I+n4ald/5I8DjITKk4V\nJ0jk+YUUonnOMeSGWKxHA/+aXzj4Mqjlavzv4Ivw+r1YXv4B/rzpnoiylHZnO277bhH+vOkeyW1a\n7M0o7yjDuZmTkR6XAZffJcmu2HmJL7mBElZmbv58AGwiYfNYMemDsZj8wTjBgDOXqGcGAIpThuGL\nhd8g05BFe6GkemZsHsJ6C9dWca8NkZj9YdyfoZKrQizb6ecJM6QXYJORy4ZciX9MDlqDilnCC/Lm\n0X+Tadu9xdz8C6FX6vH5sU9i7vWo52nmqbUul3AQyWeXqxMWhwVLy95FjiEXlw+5ijKSBpWRzgOb\nnTcX/1fyO5oohGdm2OudJOv873JtzXdUKUCuK5LYyiDDnzbdLZBFkXMmz5iHjy/5EnPy5mJc+jlQ\nyBVBSQw3L2oLT7LoC/iiDhgl+z1o2U/XsD3Nu+AL+LCel+AQfF+9WsBI9gZ8G/C+MDNH24+g1dGK\nso6j0Cg0NBk2qA043lWJq75eiBZHC1ZVrcSlKxbQZIRIqoh8l8xwCzABlLWXYUhiIRI0iegS9f2J\nB2p3u7uwo3ErtAptyIy9viIjLhNNtkZsbtgIAJgq0S8TK0akCo/L5rXi02PL8WYpa/xx0LIfLp8L\nh9tKMThxSNTicaI2CS/OehXx6gRcPGghnp7xHO4cc3efji1Fl4Lrim9EnbUWOxq34ZJBl2LJBe/R\n2WAA8MyeJ3He8in4/fo78M7hJbRfB2CT4Xh1PGQyGRYOZq2qR6aWYKSpJOz+7hh9J47cXEkVQvVW\n4bVAij/iNYrEPW3ONlRzhYFPjn0U01oTHBESLBibosw0JDjSxtqN94eZyUvIp/++bMiV9Pcl8cPx\nrkrJkQX8a7LV0YoNYa7/cDjjyUyTrREOnwNDuAE85IYmptx6i8emPImPLvqcJjVAMJkh1TGLw4IN\ndetQYhqD64tvAgOGyht6YwAAADePuE0gP9KLhliSSgW/74DgxmHswKGHJj2KKwuvwaLh0Rsm+wo+\nM/PwZFaT2Mg1PvOHZgKgw7S63V14fOqTeGzqk7h26A0AQisv0SCTyXDxoIWIU8XRC0xoAJAlOBaA\nDeZ0Sp1gu4KEgdh09U78bnTfFrJwIImzuKrGBxl2NZjnuc++VlhllqrmkkA4RZuCt+YtFVS3p+fM\nBICQSijABqtfVX5BJ48DbLKbwwUu62vXChy5LE52GrsUc8Wv3CVpksNu896FH+HzS1biien/wfqr\ntoZNePoLUqn5+vgKtLvawYARJBRi3DT8Flw38jqUpI0O+3yWIRs3FC/Ca3PewgcXfYqPL/4SGXGZ\neHbvU2GnEQPBqc16lV4gseTPgfrXtKdR/Ztm7Lh+X6/lG/x+pJK0sbiy6Bqc6K7CxV9cgLvWL8a7\nR5ZgbfV3Ia/rdneh3dmOi76Yg6+Of4F3jyyhNxgxyBytGbmzolYVwzEzJFlZxE0Qr7XW4Ej7EbS7\n2lFrrcGlKxbggS1/ht1rp9+XlHsX6TWSkpmR6yukZ4a7hqxckLmu5ntoFVrMzJ2FgoSBONZ5LOwN\nPFz1kY+5+fOhlquptp2PbF5/SV97M+NUcZhfcBGqe07g7g2/FQT+UqgTNQCn6zMwhhtcS36/dmcb\nnt37JNsnMPYPUClUyDRkIUWbghGpIwWFhL+f+zA1KQjXozYiZSSeO+9lPHAuq/zez5vxsKNpG+2d\n4xdhjOp43F7yW5zorsJ/9zyNis5jcPlc9JxJ0CTCoDJg2YJPsOoyNtDgS2IAYHn5BwCAS7lgL9Ig\nQjIbi4AEL/WcjFasr2cYBovX3IY71t7WJ8MAgcysl8yMzWvD/M9m4Y8b78KxjnIMMw2j6wJJqjfW\nrcfsj6fhlm+vhwwyvDf/QwzgsbpkNhmRmVV3V8Hhs2NYyggkahJDKvftImYGYBvnR5lG96pfNhKG\npQyHw+fA0qNss/bUXpgKhcPwlNCq/j0b7oRWoUVWXDaOth/B4z88ApvXijn588K8Qyhm5J6Hytvq\nsGTue1g0/NdhVQWx4ndj7ka6PgPXF9+EV+csoQYWJJlpd7XjSHspPipfhr9svgfTPzqXzt7p8fTQ\nxGNocjHeuOAdvDJber6XXCZHvCaB9k0TiR1Bt7ubHfwrUvMQVUCLo5kmtJVdFVjJGYJEQrhCj5g9\nlcKR9kNQypUoTA4vM4wFBpWB7u+GYmFxsjh5OPyMX3L2ED/ZququpMM2o+GMJzMVtF+GpdGt3ELT\nH5kZwDIaswbMFvTdUAMAJ3uz/7LyU/gZP64svBpjucZlElD2drrsNUOvx7org2yFXiUMAItThmH1\n5evw6SWh7jfPzHwe+246gtl5c/Hy7NdPSfBIwA9yL8ifR5MuuUwe0ieUzqvukwRrWs4MvH/hcrw7\n/4M+H0Oq3oRsQw5KTEEtabo+AzLIhMyMsy0slVyUPFSyN6cvMNJgygpfwBdW91zJVWbEjBDf1tOk\nS5O0+Zyecx4KEgbizbnv0UWNPpc7EwDw9uE3sb52rYAhunvDb3Hb94vwQdn7+KbqK4w2jcG07Bk0\nGPvH9gdw/aqr0OnqwCsHXkSp5RBSdCmSgXeucQB9LkkbPpmRy+RQyBUsKyPqJzlZmJk7C3EqA1ZW\nrUAlZwAiZRhAtl922TLJPjqZTIZnz3sRlw65gm5/VdG1CDAB7OKkU2IQxxedUi9I5ImpA4Fepe9T\n/14S72abFZeFO0ruAgDB4DS+wQbAyizHLx2FUe8W4nhXJQZwTjRvH34z7D5I0/70nJn095z32ayw\n080JMwKwCQfDMNjeuAWZcVmYmj0DAMscHG1nE6f/G/VbFCYV4c3S13DlV7/iJTPh1yfSXyQ1AJMy\nM+pQNzP2mHpQb61DWcdRTMmeBr1Kj8KkobB6esJW0KMlM0Z1PI7dWounpv837PMvznoVJaYxAtOK\n3uKhSY+ixDQGH5Uvw4WfzcaJ7ip4/B68dfiNsIEycTMy6dKQFZeNTy/5iq5lpMK/sW493j3yFgYm\nDMKNxTcDYK/Jry/9Hq/OERoOaBQafLjgM7w9bxltnOZDJpPhuuIbMYmTXpOBdcXJw+EL+KiEg1/k\nGGDMw18n/A25xgF4bt9/MOXD8fjXD4+GlcOQxCqYzLSh3lqHzfUbcU7GRMzOmwsAON5VgYe2PYAP\nyt4POUaSABG5Fhl8Stic8o4yQdJSb6uDzWtFm9OC42EmqkdDTz96Zk50V8Hpc7JFJL8LI9KC1WsX\nL5ltcTRjSFIhVixcjQvy52MY99kUMgWV7xCZ2RHuehueOpJjZrro52UYBh2udgxOHIJp2TMEsjL+\nv/sL0rNyuO0QMuIyJRnwWMFXBpAir9vvxr+mPY3z8y6A2+/GawdfRl58Pv58Tq9nrPcbucYBOLio\nHP897yXBvZKvXBltGoNNV+/E/RMehDfgpcNqu93dgl7KXw2+DEUxBP5EeUMYOoJudxcSNYkhagel\nXIlkbTJ2N/8Ah8+Bkakl0Cv1uGvdYsniFoGDqg74zAzXMyMhM/MFfPjblr9gb8selJhG97lnneDG\nYYtwddF1KOH1DOH/2zvvwCiq7Y9/drPpBUJ6IKHnAklokaYCIh3pICAqUuz61KeoPMuzPHuv2Bu+\nnw1FfYioD/ujCigKwgWUpjRpAkIoSX5/zM5kN9klhU1mk5zPX2RnZ/cyZ2bvPfec8z1Am0Sj1mrV\nrmJB5ILCAu5fcjeLti30eiZnrZvJscJj1kbZibDVmXlv7Ttc6O6obOYEm8Xh41qd6/e8ymKmopg7\nHe+ufZsQRwjDW462irTMfMGKOjMAaTHFaSguZ+k+C3kpnfwuIKuL5KhkHu81nS/HLCA8JJwnzpzO\nwKaDuaHTTaUeJrP4v3FcE68doP5NBp7UIjc8JJxl569kaqfiItOwkDCSopK9IzP5uwJaG+MPc6f4\nwNEDXPzZJPJezy7VpfYXtxxy81KRmWLnpUNyR/zROa0Li8/9waeMdtO4ZgxoMoifd69k3EcjOeXf\nucxY9Qord/1kpVjc+M21FFHE3zpei8Ph8FIp+va3r8h5tSW3L7iZgqLjXNL2Cr/jcDldVpQo/iR2\ntk6WCFcE/Rr3Z9P+jcxca6R6+lqMnQzd0o0FXMnCWxNzlzw5KtmKzHjKzJ4snhNeWkxDshooK73p\nYndBd8mcfXPReKzwGKeld2f++KVkxGby7tq3S0U8ioqK+HrLlyQMHOg3AAAgAElEQVRGJtEmIduK\ntO04tN1nd3PPyOO+I/tYs2c1uw7v4rSG3YlwRZASlcrmA5utfOYxrcYz7+xv6Z3Zl6U7lvCZu86q\npJJh8f/XmHBKpsWYmCIbJZ1Fz/S0z90L2T6N+wGQ5Z4X9J41pT7vkIealj+iQqP8puqObTWe/579\ndaX7jIDxmz97xKdMaDOZVbt/ou/Mnoz6zxCmfXMd/1pUWgdnizva8Pqgt1h2/kqvRVCWu+B65tq3\nOF54nEvbXel1L7aIb+kzipQUleS3hs3EdDbMtLJJOUY6tLkLWi+svvWejLhMYkJjeKDHI9b5n22c\na8me+9oRjw+Px+lw8sfhnbyt36CIIs5pdZ4V8Xlt1cs8u+IprvnyCh767j4v58QcQ/dGPUmPbshX\nW76goLDAquU8eOyAFaUBWOtxLyzaVvrZ9vzsPfm7OW/OGCuVGryd7U37N3qp15WFmYZuqgd6OjPm\nhte1edfz1diFfDtuibWQM4vlm9Vrbt1vZmTGXNRluyMzxwuP85d7o+XA0f2WSuZ7w2Yz0r1ZA97F\n/ydLjkcB/snWy4B36whzzhzRYhTntp5gRaYA7jr9/kqrkp0snhL/Jp7rs+zEXFontOHqvOtoWq8Z\nH66fxcsrX+DQ8b8qlTnU0e18Ltvxndfrfx790+9iPTEyybo/z1ZjebL3cxw6/hcT5o47odT/X5YA\ngK/ITGlnZl/+Xs75aBQv/PQsKr4Vz/SpuEpjSaZ1uZUnez9b6vW27uwkz5TXjzfM5uGl93PRpxdY\nrScAPv7VqNPuV47ona3OzGXzLrRk7swC3gtzL2XJuSsYlTUm4N9XLM28m/V71/H9zuX0zOhFclQy\n9cLr07J+cZFlRWpmPFl2/kpu6vJPTks/uTBtVXJO6/OsfgtDW4zgtYFvcO0pN5R6n+lQmupAgcTX\nD0l6dDpbD/5OYVGhldbiS8M80JiLq/1H97Ng67fsyd/DqP8MZcm24h19M2e2ZIGlpzNjFgRXFIfD\nwWsD32TuqM85v81E9h3Zx9Svr+bm/xk2CXGEcLTwKM3rt2BQ08EA5KV24pxW5/FU7+dIiEggLTqd\n27rdxQ8TVnN13nUn/D4z/amiaVOBxpSKNqMIrRv4V8epDJ1Su+B0OFm4bb7P4yt2Gj+m7ZI6WD/0\nJxsR9sQzwmqmUT7c60le6v86l7QzHE7P3fsVO7/nHf0mOYlt2XjRdmYO/ZDwkHAuyJ7MoeN/lRIP\nWLtXs+PQdno06onT4SylTFcSzzSzg8cOWCm1ptJgo9gMth78jZW7VhDiCCErXhHhimBaZ6MX0gJ3\nnZy/yHHrBm1w4GC5uzi5JGbNTsnIjJlm5unM9M40nJmW8QrAkiP3+rxjvgUAqpsIVwQPnfEYT575\nLMcKj1ppEQt+L11XaNbMNIrNLOVkNYrNIMoVbS2UTaW4QJBYoh7yrGZDvURCnE4njd3iKpnujare\njfvxw4TVdE7tyoY/f7XG7qv+M8QZQkKEUUT+1pr/I9IVybAWI2iX1IH06Ias2bPaOveB7+7h9gW3\nWE5HsQJjKr0b92Xvkb18v3OZlwPjmWqmPe4FzxSUoqIi7l18J+1mtGLFzu95bsXTfLbxEz7b9AlT\nPj2f+5bcRWFRIfvdEabuDXuy5cBmZqx6pdzXceP+DV5/5yYXp1OZkaVBzYbQJiHba0Hf2u3MtEpo\nQ7jT2PH2FZkx08B7vNmF+b9/a228mq+bMvIQ2MhMjkexd/dGPQPymYPcdZ03dr6ZB3o8ysO9nsTh\ncNAhxUirDHWGetWuBQOev22mA+p0OJmUcyH5BflM+8aYW/0pdZ6IxMhEMuOa8P2OZV4OtxmZ8YVn\n6llOYluGNB/GjZ1vZsuBzUz65FyOFhzlWMGxUueZWQfRPmpmSkZm8o/nM/SDAXz925f0bdyfj0fN\n89nDLlBkJ+YS4gixFGCLioosYYMdh7bz6qpiRyq/IJ/YsDgrFf9E2OrMqPhWnNVsKE3imlqOhKFc\nVDUXMj48nujQGD7bOJdT3zQeqLOzxlnHO3qkG1QmMgNG+PKavKkBL9y3g9FZY1k5cT1D3IvOqqZJ\nvaYcKTjCtoNbi5XMKqlYUhHMGon1e9eyJ38PmXFNOHTsL8bMHs7zK6bz0Hf3sXr3KjLrZZZKb/NM\nzevnLqKuDA6Hg7yUTjx8xhPc1/0hABZunU/rBtlWMf2V7a+x7qvwkHAeP3M6Y9Q5/HDBGpact4Ir\nOlxVrsifr54QdnBmZl8iXZEUFBXgcrq8FJsCQWxYHDmJbfl+xzKfP/g//PE9Ua4oWsZnWT/0cQF0\nZjwxlbVSolIY0nyYlYZhhtSLioq4bcHNANxx6t1EhUZZzub41hMIc4bxysoXvSZBM2rXs5GhFFjS\n9iV3nD3TzABLndB0ZjJiMzheeJxlO5bSMj7LSjNol9zBK+ror2amfkQ8bRJyWLbjO5/FnQcsZ8Y7\nEpIWbUQb1u5dwzdbvqJF/ZbWHGDmbfvKr/YnAGAXY1uN5+ORnzM6ayxp0en8dnCLV2+ED9a9x5Jt\ni4gIiShVnwjGokk1MJy3KFe0X2ncyhDtirbSXMBYWPVu3Nf62+VwWWp9ZmojGPVE3dJPo4giy9Gs\n52fhlRiZxIY/f2XT/o0MaT6c2LA4XE4XE7InAUbq0ZdjFpAVr3hmxZPW/W72IkmOSubMTGNM8zZ/\n5qVsacogA2i3Y+R0OJm1bibXfHEFRUVFPL78YR5d9hDb/9pG33d7cuv8f3Dz/24EjOv5yNIHmPTJ\neZZoysNnPEFsWBx3L76j3L+FG0vU/nhGZt4c/B7vDv2PV6NVk27pp5MZ14RBTQdbWQ5HC83IzEqS\nIpNJjkrmig5XM6LFKLYf2sbo/wzlsWXGXGA6Mynulgmp0WmVrvXyRb3w+lZdT0WacJ+IZ/u+xJdj\nFnB6wx5MzJlibRDnJrblub4v8915P550BKgqae0hPX1R7mW8Pqi4iWNl54m85Dz2HtnLk98/ypWf\nX0K3NzpypOCI39RdM7KZm9jOEgq5Nu8GhrcYyeJtC5kwdxytXmlq3ScmvlJwjXrlaKtm5ljBMe5e\ndAdvrvk3a/asZniLkcwY+FZAN/R8EemKRDVozardP3G88DiLty9i+c5lnJnZx+eGXPeGPcs1Jlud\nmW/GLeaVAf9m8bk/+J0gA0mIM4SX+s+wFhJRrmgGND3LOn6FhzpGYjWkNwU7DofDZ7+SqqKZux5l\n/b51Vh51daSZmYurJdsXATAmaxwv9p/BscKj3DJ/Gg98dw+783ejElSpc8NCwph6yjSm93nhpHNM\nTYY0H26Fna/scDU3db2NB3s+5jfyEx4S7jPS5Q8zTaRkM7bqJjo02tqBb16vRcCKWT3JScjlaOHR\nUgXIh44dQu9ZTU5iW1xOl7W4DLQzY6b0lfy/hYeEkxCRYEVmPtn4MQu2/o9+jQeU2hlNjExkaIsR\nrNu31lIfM86ZgwMHvd0pWSWdmZJNxszIjDlhLNq2gMzYxlb/nEYeqaMle0i093Jm/Nf0nZp+GvkF\n+VaxrK/vLxn1NkUdXln5IoeO/2X9f8AQ3HDgQO8tnWZWVs2MHWQn5jC9zwtc3PZyoLgG89kVT3Hx\nfyfxx+Gd9G0ywO8iTrkdjrZJ7QK6IeZwOHisl9HLKsoVhcPhoE9m8XV2OV1WzZpnsToUN4U2e4L4\nEw7xjP6YQjEA57WZSGJkEpNyLqRhbCM+GD6XjNhMXl35Ild9cRnXfXUVYDg7PRr1xOV08c6aNzlS\ncMRyDMwGlWA4vaHOUB7o8ShpMQ15Y83rXDZvCvcsvpNGMRlec7fZv2jWsNmc3rAHczd8xJLtiwhz\nhtE4rglXd7yWP4/s4/kfnynXdTTTzMAQvGgU5y0k4W8HOTkqmaXn/ciorDHWWufI8Xz25e/lt4Nb\nrEyJNgnZPNfvFWYNm0ODiATeXPNvoDirJDMuEwcOuqR28/k9J8OV7a9mcs5FFZag90eEK8L6f5Vk\nRMvRAXXGqgLPTIEQZ4hXFKmyartmNO2uRbfzjn7Tqvnyp5R5Y+dbeLr383w6+ktrfWE8y9Npl9SB\nLzbP48DR/aVq0Tz7zHiSFJVkRWY+2fgxjy9/mBu/MZqfjmt1XrVtwrdLas/h44dZt3ct091RmWvy\nrmegO/PEkzMz+5SrjtxWZ8b8Qa9O7/zMzD4sHL+cp3o/x2sD3/CaCFs1aM3Pk37lpf4zSunzC1WP\nqRT2y5/rrchMdTiV5gLWzL9uk5DDWc2G8Obg9+jRqJf1vqwE35GDGzrfZElWB4Ko0Cimdb6ZQU2H\nMLzFKFKiUrgge3LA0sJGtDDyrk8kn1xdmE1fA10vY2Lu7Ou9xm7uvvy9LNq2kKe+f4yCogIr4mBG\nZgK9K7X43B/YeJHvIuPkqFS2H9rOsYJj3LnwVkIcIdx26l0+32vWOJhFqLsO72LxtoV0Su1ibTiU\ndKY3l1DNMSMZngsuz11Yzzq4Ic1HeJ3rKQHrr2YGjB1ogDk+FHfMyEzJa9woJoOEiAQr3chzkR3p\niqRlfBY//rHCq1fJ8h1LrUazweTMmJzh7qv13rp3eH7FdP45/yZSo9P4euwiXuo/w+95Kt5wZkyF\ns0DSMeUUZg75kPeHzbHGaP6mhDhCmJR7MTd1+Se9M/t6nVcynclfZCbJ/VvdOK4J3Tz6+CRHJbNq\n4nquO8WIkiRGJtKncT/yC/K9hCqSo1KIDYujc2pXK8Wsa1o3olzRVmRm68HfWb37Z5rXb8GE7EnM\nGGikXs5a9y5Jkcm8O/RDxnk4UmCkM7VNas/bg9+3HLUIVyQOh4PJuReTEJHAcyumlys6s3H/BpKj\nUkiOSqFzapdKrV3MjY0jBUet+rSS6l9d07ox7+xvLHEKs4dQanQabw95n3+d7r+PV2WZmDOF+3o8\nHPDPrWnc2/1BLsieUirF3eFwWGl+Wz2EiirC2Fbjubz9Vdzf4xG+GruQTRfv4LFeTzO1040+39+0\nXjPOVuNKzf1RoVHMGPgmA5oMAoz5wDMS72+jJykymV2H/6CwqNDaMAYIc4ZZ0tHVgVlL9n+rX+OT\njR+Tl9KJLqldvbKAzAySXpm9/TbU9sR2NTM7iHBFMEadQ8+MXqWOJUYmMqT58FqRJlbTsJyZveus\nXOHqSDMzF1fm7r2pttGj0Rle6nP1KpEnW1kubHsprw78v4AVo3syuPlQFpyzjL/nXR/wz64oA5sO\nZnyr85mce0mVfH4r05nZs4ZXV75E1suNGfp+fx5aeh9QvPiuipoZMH5r/CnvpUancuDofp5Z8SS/\n7FvPhOxJflPtTknpTG5iOz7ZMIdN+zfy2ca5FBYVeu1kdUw5hV4ZvemV0RuALfs3eX2GmWZ2YW6x\nOIBng2HPJpP9S6RMekZqTrRL1jPjDNKi03nux+k8/f0T3t/vFgAoGZlxOBzW5BbliqZr+qlex7uk\ndeOvYwdZtesn9uXvZepX1zDwvd5W/UJlO4FXJdmJOZyafjpfbJ7HLfOnkRKVyvvDPirTae/fZCAt\n62cxouWoKhlXz4xeVs1CXHg9uqWdhtPhdAtApHBN3tRSvzmJkYk09Ohh4y+/33yGxrU6t1SkuOSi\nv3Nq6Xogc6HY12MHPCM2k9YJrVm/by2Hjh3i4s8mcej4ISbnGC0X2iRkM67VuSRFJvPOkA9oVr8F\n151yIy/1n2HNJ03imhoNKUNCGeC+r81a3ZjQGC7vcDX7j/7Js27n2B9HCo7w+8HfaF6/BZ+f/S3P\n9PWtMFgWYe5Nh6MFRzwaFJaOYKTFpPPB8Lm8M+QDhjQbbr1+RsaZXrUzQmCZknsJD/b0rYA4vrXR\nGLNki4byUi+8PrefeheTci6kTUI2ka5Ixrc+v5SwUHlIi0lnxqC3GNnybA4eO8Cm/RutY8V9ZkpG\nZpIpKCpgb/5eL4noTqldqnVTaGCTswhxhFgR0cvbX4XD4eD0hj0JcYQwrPlIuqWfTveGPcmIzSxX\nZMbeCmBB8KC5R5qZ2USzWtTMPOpeolxRpSSCH+r5OFO/vprRbUaXPLXG0iK+cj/GgSbSFcljZ554\nEXEymApRes8aFh0xioUvb38VrRq0Jjsxlxz3jmiz+s3pmJxHr8zeVTaWkqREG87DXYtuJzYsjus7\n3eT3vQ6Hg8vb/43L5l3IE8sftZTNPFVeIl2RvD3kfb7YPI8vt3zO5gPFzsyuw7uK04Qi4vloxH95\n4cdnrMgYGKIScWH1uCZvaqmdwNYefaVOlBIcGxbH+8PnMPKDwdyx8BYKio5zVUcjjaE4MlM6RaO9\nO2WiR8YZpSJMnVO78vrPr3L7glvQe9ew6/AftGrQ2ioq9+ydFUxc3+kfjPjwLJKjUpg17KNyLVha\nxLdk/njfAgpVwUNnPM6aPav9RltMcpPaWVLJ/jZYBjYdzKrdKzm/zaQyv9dT3OAfnW+lSb2mVhR+\nUs6FRLjC0XvWMKLl2eg9a1i2YymXzpvCku2LGNZ8pNUXCeCxXk9TUFhgjSs6NJohzYcz+5cPWL9v\nnZecfv8mg0qllE3OuYhnfniC5398hkvaXu63f8l32xdTWFRIqwatrWe3MhQLABzxG5mx3hsSbkX5\nBPu5odNN5Ca2s5opBwM5iW2ZtW4mU7++hszYTA4fP8SqXT8RERJRalPeU9Fsy37DmcmMa8Lk3Iuq\ndcxpMen0azKQuRs+orG7lgyMqOWGi7YRFhKG0+G0akTLs2ElzowQNMSGxZEclcJPu1awx934r1rU\nzDyaJLZq0LrUruKE7EmMzhpL47QU/vjDf2NNIfhoGNOI6NAYVu9ZxdaDW2lZP4vbfaRyRboi+WR0\n5TqKV5bUqOLd1as7XldmSuXwFqN4aOl9vLXm30S6okiPbuhzh9As4DYnq/1H/qT/u2dYO3ExoTG0\njM+ic5p3Km1KVAprp2zyWX/lWbR/opoZMORn3x8+h5EfDuauRbdTUFjA8JajLNWmkv2sAM7I6M0j\nyx5kZIvSGwZmpGb+1m+JdEVyS9c7uLTdFew/up8l2xaVq8eDHZzWsDszh3xIy/isoK0PaFqvWSmF\nRl/kJrblkw1zTvieUxuezqyGH5XrexvFZJAWnc6OQ9uZnHuRlzMVFRrFFI9IrSlc8MmGOTSt14xH\nej3hFelxOpw4Q0rfs20Scvhg/SyrFhOMSGSDiAZWPyownJ8r2l/DHQtv4dkfn7bU+0oya+1MAIaW\nSMGsKMUCAEdZtesnwpxhld7pF6oXp8NZpgx6dWMq0X3r7jlm4hl1N/HsNbP5wEbCnGEsOfeHCtXc\nBopL2l7Opxs/5tq8G7ycLs/NMvM5l8iMUONoWT+L+Vu/ZdfhXXRMzvNSFKkqPCMznrUBngSySadQ\nfTgcDlS8shpV5qUOLeOM6sPTib6s3ZVlvj/EGcI1Hafyty8u5djRPxnUbLDPnP1GsRm4nC5W7zF2\nff/x7fVeKQUnKl490aR2S9fbWbN/JWHOsoUamtZrxgfDP2bkh4O5d8m/uHdJsby7r+/vmn4qqydt\n8Ll5kRnbmMzYxmw+sIlZwz6yajgSIxMZ1Kx0wWgw4SuVuSaS6+4NESgcDgf393iEPfm7y4wKmXNA\neEg4L/Z7rdypoL0yenP/krvp4SGoERoSyupJG0o9NxNzpvD0D4/zyNIHeGP168wd+bnVmBiMKMrs\nXz8kLTrd5yKxIpiLtUPH/mLNntWoBq2rJJ1YqBuYz6YDB5+P+R8JEQlEuiJ9ykeb4ld/HN7JlgOb\naRSbYYsjA8bmx7opm8v1PEeWo2ZGnBkhqLix883M2/QZQ1sM9ylxWRV4PkwlVZyEmk/fJgOKnZkA\n9mY4Wc7OGsuv+9b7rFPwx6isMTy09D427d/oVzkpwhVBp9QuLNq6gJdXvsDMtW/RIbkjN3S6ifX7\n1lW6SeRVHa8lKSm23NHJxnFNeH/YHM6ePcxLTS46zLeUsr8orMPh4N2hhqBAVfY/EPyT69FUMVAM\naDqoXO/LS+lE78y+jFHnkJtUfqeqXXIHfrtkV6lUG18bANGh0dzb/UEe/O5e1u7VvPDTs14RXFMg\nYEKbSSddT2vWzKzZs5r8gnyZc4STIiEygf+O/ppGsZllZrKYaWYb/9zArsO7/KY3Vhfl3ZiQyIxQ\n4+iafmqpAuCqxnOnuDoiQUL1cnn7q7h/yd2AUUgfLKRGp/For6cqdI7L6eL+Hg/z7IqnT9hwrndm\nPxZunc+0b64jyhXF9D4v0Lx+Sy/Z4+ogM64x889Zyu7Du8h9zRA3iHZVvNBUnBh7SY9pyC1d7/Bq\nrlhdRIVG8ebg9yp1bkUcj2EtRjKg6Vl0nJHNv39+jamdphETGsPWg7/zyNIHSIxM5KqOf6/UODwx\nI5vL3fLl/uSLBaG8mAIqZWGqdpqNjUvKsAcrQS/NLAjBQFhImCU3W1USwYJ9RLoi+WrsQu7t/mCt\n2AU9M7Mv7wz54IRdqD3lde847Z5KqeUECpfTRUp0Kneceg+XtL08qBvlCb5xOBxc1fHvQVX4XBWE\nh4RzfpsL2H/0T77eYtTQ3Tb/Zg4d/4tbu95ZZkpceb8DiuXi7d4dF+oOZs3M8p1uZ8ZDjj+YcTgc\nZcozS2RGEChWTQtWZSTh5GiTkF0rHJny0iYhm9PSu5MancaEcqhLVQeXtS+7LkgQ7ObUht15ZNmD\nfL9jGTGhMXz4yyzyUjr5bVpcUUwBALMviERmhOoi2Z1mtsvdx89smFwTKCs6I86MIADT+7wQsKaU\ngmA3DoeD94efWH1KEITStE8yUnaWbF/E3A0f4cDB/T0eDlihtKf0eFp0umygCdVGdGgMka5IDh8/\nDHg3Sg52ypJnltWbIIBXx2pBEAShbhIXXo+W9bNYtG0BABOzpwRUjCY8pFh6NtuPeqYgVAUOh4Ok\nyGSrB1lGLYrMSM2MIAiCIAiCmw4peQA0iGjAP7rcGtDPNtPMALJtEFQQ6jZm3UxESISVdlYTKEue\nWZwZQRAEQRAEN6c37AHAbd3uIj6iQUA/2zPNTCIzQnVjyjNnxGbWKDEWqZkRBEEQBEEoJ2PUOXRO\n60qzes0D/tlhns6MRGaEasaUZ65Jxf9QduPycjszSiknMB1oBxwBLtRar/c4PgT4J3AceFlr/UJl\nBiwIgiAIgmAXToezShwZgHB3mlmkK7LKvkMQ/JEUaaSZ1aTifwhsmtlwIEJr3Q2YBjxsHlBKhQKP\nAv2AnsDFSqmUCo9WEARBEAShlhIeEoHL6aJNQnaFmnoKQiAojsw0sXcgFSSQaWanA58AaK0XKaVO\n8TjWGlivtd4LoJT6H9ADmFmh0QqCIAiCINRSQkNCea7vKzWmYaFQu+iV2Ye8lFMY0GSQ3UOpEI1i\nMk54vCLOTBzwp8ffBUopl9b6uI9jBwD/7andxMdH4XLJzkR5SUqKtXsIdR6xgb3I9bcfsUFwIHYI\nDipjh8lJ51XBSOou8iyUn6Sk9ixt8V0VfG7V2uD+QXef8HhFnJn9gOdonW5HxtexWGBfWR+4d++h\nCnx93SYpKZY//jhg9zDqNGIDe5Hrbz9ig+BA7BAciB3sR2xgP9Vlg6SkML/HKlIzMx8YBKCU6gr8\n5HFsNdBSKdVAKRWGkWK2sOJDFQRBEARBEARBKB8Vicy8D/RVSi0AHMAkpdR4IEZr/bxS6lrgUwwH\n6WWt9e+BH64gCIIgCIIgCIJBuZ0ZrXUhcGmJl9d4HJ8NzA7QuARBEARBEARBEE5IRdLMBEEQBEEQ\nBEEQggZHUVGR3WMQBEEQBEEQBEGoMBKZEQRBEARBEAShRiLOjCAIgiAIgiAINRJxZgRBEARBEARB\nqJGIMyMIgiAIgiAIQo1EnBlBEARBEARBEGok4swIgiAIgiAIglAjEWdGEARBEARBEIQaiTgzgiAI\ngiAIgiDUSMSZCRKUUk6lVITd46jrKKUcSqlQu8dRF1FKhSilUt3/lt8mG1BKuZRSlyqlcu0eS11G\n5oPgQOYD+5F5wX5qwrzgsnsAAiilLgb6AVuUUo8Dm7TWRTYPq06hlHIADYA7gZeBZfaOqG6hlIoC\n7gXCgMu01oU2D6nOoZQaA/wdyAHSbR5OnUXmA/uR+SA4kHnBfmrKvCBers0opdoAw4Abgb3ApUB/\nWwdVh3BPWrgXC02BMUAPpVQDWwdWBzCvvZvjQDOgmVJqiPt4iC0Dq0O4IwDRSqmPgOHAFOAdoL69\nI6ubyHxgLzIf2I/MC/ZTE+cFcWZsQClVTykV7f6zJ7BFa/0L8AzwK8aPZ4JtA6wjuK9xtMdL3YG3\ngNZA0IZTawM+rn0msAd4EBiilEoGJL2jCnHbIEZr/Rdwg9Z6PLAVyAB+t3VwdQiZD4IDmQ/sR+YF\n+6mp84I4M/ZwF3Cl+9+zMSarJlrrP4Af3K83s2VkdQSl1N+Bj4E7lVLXu1/+r9b6b8AmoLdSqpFt\nA6zFlLj2N7hfPgp8C6wC2gPvA41K7NIJAaKkDbTWPwNorfcBB4FT7RxfHUPmA5uR+cB+ZF6wn5o8\nL4gzU80opXoCZwJdlVI5WuvfMB7QWwG01kuAFkC4+/3y0AYYpVRLjNSNocCjQH+l1CSt9Ur3W17D\n2IXoqJQKs2mYtRIf176PUmo8xj0/GWM3eiuwE9gttQKBp4QNHsawwYXuYwnAWuCAfSOsO8h8YD8y\nH9iPzAv2U9PnBXFmqp9M4EVgDkYeIsB9QGel1GilVFMgErdt5KGtEpKBlcAhrfUW4DbgZqWUC8C9\noFiMkSuaZtsoayclr/2dwO1ABLAcuBsYDawBxtk0xtpOSRvcAUxTSrm01ruBeGAgiHpQNSDzgf3I\nfGA/Mi/YT42eF4JuQLUNcyfNw/gzMfJwlwFJSqkBWusDwA3AKcAbwHta62/sGG9tw13EFuP+t7mr\nuRdoDqQrpRxa6/nAfOByj1NfAV7UWm+q1gHXIsp57f8HfA101FpfqbX+DigEHtNaP2PLwGsRFbz/\nr3IffwE4RykVIupBgcHTDu6/ZT6wAbfEbMk5WeaDaqScNt6aYAIAAAWvSURBVJB5oQqp4HNQI+YF\ncWaqAKXUYKXUCx5/O03ja63ztdbbgHXA58AY980xV2s9DThNa/2qLQOvZSilrsRYKLR1v+RwP6Q/\nY4RMzwHMwtqvgN3u85xa6yNa6wXVPORaQwWv/QJgg/s8l9a6UGu9o7rHXNuoxP2/A0BrvRTooLUu\nqN4R105K2kHmA3tQSt0EPAmc5X5J5oNqpoI2kHmhCqjEc1Aj5gVHUZFErQONUuoa4H4gzyPv1syP\njtVaf+T+uyVGbvQMrfU8WwZbC1FKJQHfYOx6Puje6fQ8nodRTNgd+AVjIfF34E6t9ZxqHm6tQq69\n/YgNgoNy2EHmg2pAKRUOPAAcA54D2mqt3/M4Ls9DFSM2sJ/abgNxZgKIuePmVoQ4BYjXWg9y30QP\nYjQdulpr/ZP7/S6gvtZ6l32jrp0opd4F/oNxzeMxQqg3YhQXdgDOx5B47IaRB/qS1voLe0Zbu5Br\nbz9ig+CgDDu0ReaDKkcZfUmewuiTMRSjWfjvGBuO8jxUA2ID+6ntNhBn5iRRSl0CFGmtn3ffLOHA\nc1rr85VSyzBC1c8Ca0yZOyHw+LDDZIyGc89hqAO9jZGD+4zWeqd9I619yLW3H7FBcCB2CA5K2CET\nuAnYjKGINZdiOzzllsAWAozYwH7qkg2kZubk6QHcpJSKcucSRgLrlVLnAw6gHfCp6cgo6V5bVZS0\nwyrgaeA190N6JTAEowGX2CGwyLW3H7FBcCB2CA487bAZo0fGCGClu+7icmAwRqRM7FA1iA3sp87Y\nQJyZCqKUSvX4dzawH9DAPe6X4zEmrO4Ymt3LMdIKAAjW4qmaxgnscK/75WUY/QEauP9uDMzWWh8H\nscPJINfefsQGwYHYITg4gR3ud7/8LLANaOtesDUBPhc7BA6xgf3UZRtImlk5UUb339sxtLhnA58B\n+4BUjLzDH4EhWutVSqm2Wusf3ee1AJpqrf9ry8BrGeW0wyCt9RqlVG+MHNCGGLKO92mtv7Rj3LUB\nufb2IzYIDsQOwUE57TBYa/2zUmo40BvIAqKAf2mtP7Nj3LUJsYH9iA0kMlMRJmLkGV6N0ThrKlCg\nDQ4CrwJ3AXg4Mi6t9XpxZALKRMq2g7kr+jVGvvqDWuv+soA4aSYi195uJiI2CAYmInYIBiZSth3u\ndr/3Q63134B/aq2714YFXJAwEbGB3UykjttAIjMnQCk1CTgDQ6auKYYH+6s72nIx8LvW+nGP9/8O\nXKG1/sCO8dZWxA72IdfefsQGwYHYITgQO9iP2MB+xAbeSGTGD0qp+zCk6R7HKOK/ALjEffg3YB7Q\nWCnVwOO0CRj5iUKAEDvYh1x7+xEbBAdih+BA7GA/YgP7ERuURpwZ/9QDntdaL8fQ5n4aGK+Uaq+1\nzgd2AhHAQaWUA0Br/bnWerVtI66diB3sQ669/YgNggOxQ3AgdrAfsYH9iA1K4LJ7AMGIUsoJzAIW\nu18ai9H47CfgcaXURUAfIAEI0VoftWWgtRyxg33ItbcfsUFwIHYIDsQO9iM2sB+xgW+kZqYMlFJx\nGCG7oVrr7UqpmzFkNlOAqVrr7bYOsI4gdrAPufb2IzYIDsQOwYHYwX7EBvYjNihGIjNl0xDjZqmn\nlHoCWAlM01ofs3dYdQ6xg33ItbcfsUFwIHYIDsQO9iM2sB+xgRtxZsqmBzAN6Ai8rrX+P5vHU1cR\nO9iHXHv7ERsEB2KH4EDsYD9iA/sRG7gRZ6ZsjgK3AA/VldzDIEXsYB9y7e1HbBAciB2CA7GD/YgN\n7Eds4EacmbJ5VWsthUX2I3awD7n29iM2CA7EDsGB2MF+xAb2IzZwIwIAgiAIgiAIgiDUSKTPjCAI\ngiAIgiAINRJxZgRBEARBEARBqJGIMyMIgiAIgiAIQo1EnBlBEARBEARBEGok4swIgiAIgiAIglAj\nEWdGEARBEARBEIQayf8D9WG8xqcNb5AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118d89898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsla_df[['close', 'volume']].plot(subplots=True, style=['r', 'g'], grid=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 504 entries, 2014-07-23 to 2016-07-26\n",
      "Data columns (total 12 columns):\n",
      "close        504 non-null float64\n",
      "high         504 non-null float64\n",
      "low          504 non-null float64\n",
      "p_change     504 non-null float64\n",
      "open         504 non-null float64\n",
      "pre_close    504 non-null float64\n",
      "volume       504 non-null int64\n",
      "date         504 non-null int64\n",
      "date_week    504 non-null int64\n",
      "key          504 non-null int64\n",
      "atr21        504 non-null float64\n",
      "atr14        504 non-null float64\n",
      "dtypes: float64(8), int64(4)\n",
      "memory usage: 51.2 KB\n"
     ]
    }
   ],
   "source": [
    "tsla_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>504.000000</td>\n",
       "      <td>504.000000</td>\n",
       "      <td>504.000000</td>\n",
       "      <td>504.000000</td>\n",
       "      <td>504.000000</td>\n",
       "      <td>504.000000</td>\n",
       "      <td>5.040000e+02</td>\n",
       "      <td>5.040000e+02</td>\n",
       "      <td>504.000000</td>\n",
       "      <td>504.000000</td>\n",
       "      <td>504.000000</td>\n",
       "      <td>504.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>228.465298</td>\n",
       "      <td>232.135159</td>\n",
       "      <td>224.562637</td>\n",
       "      <td>0.037480</td>\n",
       "      <td>228.425823</td>\n",
       "      <td>228.458671</td>\n",
       "      <td>4.903847e+06</td>\n",
       "      <td>2.015121e+07</td>\n",
       "      <td>2.015873</td>\n",
       "      <td>251.500000</td>\n",
       "      <td>10.261677</td>\n",
       "      <td>10.259238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25.494660</td>\n",
       "      <td>25.272555</td>\n",
       "      <td>25.612276</td>\n",
       "      <td>2.607604</td>\n",
       "      <td>25.529992</td>\n",
       "      <td>25.505040</td>\n",
       "      <td>2.658002e+06</td>\n",
       "      <td>6.870752e+03</td>\n",
       "      <td>1.398574</td>\n",
       "      <td>145.636534</td>\n",
       "      <td>1.596009</td>\n",
       "      <td>1.871443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>143.670000</td>\n",
       "      <td>154.970000</td>\n",
       "      <td>141.050000</td>\n",
       "      <td>-10.450000</td>\n",
       "      <td>142.320000</td>\n",
       "      <td>143.670000</td>\n",
       "      <td>4.183300e+04</td>\n",
       "      <td>2.014072e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.780571</td>\n",
       "      <td>6.225096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>210.310000</td>\n",
       "      <td>214.517500</td>\n",
       "      <td>207.070000</td>\n",
       "      <td>-1.252500</td>\n",
       "      <td>210.717500</td>\n",
       "      <td>210.310000</td>\n",
       "      <td>3.185177e+06</td>\n",
       "      <td>2.015012e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>125.750000</td>\n",
       "      <td>9.184959</td>\n",
       "      <td>9.021982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>227.615000</td>\n",
       "      <td>231.825000</td>\n",
       "      <td>223.430000</td>\n",
       "      <td>0.065000</td>\n",
       "      <td>227.825000</td>\n",
       "      <td>227.615000</td>\n",
       "      <td>4.241794e+06</td>\n",
       "      <td>2.015072e+07</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>251.500000</td>\n",
       "      <td>10.049454</td>\n",
       "      <td>10.060945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>248.480000</td>\n",
       "      <td>251.910000</td>\n",
       "      <td>245.557500</td>\n",
       "      <td>1.370000</td>\n",
       "      <td>248.852500</td>\n",
       "      <td>248.480000</td>\n",
       "      <td>5.803944e+06</td>\n",
       "      <td>2.016012e+07</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>377.250000</td>\n",
       "      <td>11.408159</td>\n",
       "      <td>11.387866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>286.040000</td>\n",
       "      <td>291.420000</td>\n",
       "      <td>280.400000</td>\n",
       "      <td>11.170000</td>\n",
       "      <td>287.670000</td>\n",
       "      <td>286.040000</td>\n",
       "      <td>2.374241e+07</td>\n",
       "      <td>2.016073e+07</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>503.000000</td>\n",
       "      <td>14.931148</td>\n",
       "      <td>16.660318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close        high         low    p_change        open   pre_close  \\\n",
       "count  504.000000  504.000000  504.000000  504.000000  504.000000  504.000000   \n",
       "mean   228.465298  232.135159  224.562637    0.037480  228.425823  228.458671   \n",
       "std     25.494660   25.272555   25.612276    2.607604   25.529992   25.505040   \n",
       "min    143.670000  154.970000  141.050000  -10.450000  142.320000  143.670000   \n",
       "25%    210.310000  214.517500  207.070000   -1.252500  210.717500  210.310000   \n",
       "50%    227.615000  231.825000  223.430000    0.065000  227.825000  227.615000   \n",
       "75%    248.480000  251.910000  245.557500    1.370000  248.852500  248.480000   \n",
       "max    286.040000  291.420000  280.400000   11.170000  287.670000  286.040000   \n",
       "\n",
       "             volume          date   date_week         key       atr21  \\\n",
       "count  5.040000e+02  5.040000e+02  504.000000  504.000000  504.000000   \n",
       "mean   4.903847e+06  2.015121e+07    2.015873  251.500000   10.261677   \n",
       "std    2.658002e+06  6.870752e+03    1.398574  145.636534    1.596009   \n",
       "min    4.183300e+04  2.014072e+07    0.000000    0.000000    6.780571   \n",
       "25%    3.185177e+06  2.015012e+07    1.000000  125.750000    9.184959   \n",
       "50%    4.241794e+06  2.015072e+07    2.000000  251.500000   10.049454   \n",
       "75%    5.803944e+06  2.016012e+07    3.000000  377.250000   11.408159   \n",
       "max    2.374241e+07  2.016073e+07    4.000000  503.000000   14.931148   \n",
       "\n",
       "            atr14  \n",
       "count  504.000000  \n",
       "mean    10.259238  \n",
       "std      1.871443  \n",
       "min      6.225096  \n",
       "25%      9.021982  \n",
       "50%     10.060945  \n",
       "75%     11.387866  \n",
       "max     16.660318  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsla_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2.2 索引选取和切片选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-07-23    220.01\n",
       "2014-07-24    223.25\n",
       "2014-07-25    222.72\n",
       "2014-07-28    224.25\n",
       "2014-07-29    226.61\n",
       "2014-07-30    221.92\n",
       "2014-07-31    229.26\n",
       "Name: open, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2014-07-23至2014-07-31 开盘价格序列\n",
    "tsla_df.loc['2014-07-23':'2014-07-31', 'open']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-07-23</th>\n",
       "      <td>222.49</td>\n",
       "      <td>224.75</td>\n",
       "      <td>219.43</td>\n",
       "      <td>1.33</td>\n",
       "      <td>220.01</td>\n",
       "      <td>219.58</td>\n",
       "      <td>3088731</td>\n",
       "      <td>20140723</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8.977539</td>\n",
       "      <td>8.459104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-24</th>\n",
       "      <td>223.54</td>\n",
       "      <td>225.10</td>\n",
       "      <td>220.80</td>\n",
       "      <td>0.47</td>\n",
       "      <td>223.25</td>\n",
       "      <td>222.49</td>\n",
       "      <td>3248410</td>\n",
       "      <td>20140724</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8.812894</td>\n",
       "      <td>8.249168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-25</th>\n",
       "      <td>223.57</td>\n",
       "      <td>226.97</td>\n",
       "      <td>221.75</td>\n",
       "      <td>0.01</td>\n",
       "      <td>222.72</td>\n",
       "      <td>223.54</td>\n",
       "      <td>3090383</td>\n",
       "      <td>20140725</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8.641804</td>\n",
       "      <td>8.032799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-28</th>\n",
       "      <td>224.82</td>\n",
       "      <td>232.00</td>\n",
       "      <td>221.40</td>\n",
       "      <td>0.56</td>\n",
       "      <td>224.25</td>\n",
       "      <td>223.57</td>\n",
       "      <td>6517611</td>\n",
       "      <td>20140728</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.735052</td>\n",
       "      <td>8.216171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-29</th>\n",
       "      <td>225.01</td>\n",
       "      <td>228.30</td>\n",
       "      <td>224.86</td>\n",
       "      <td>0.08</td>\n",
       "      <td>226.61</td>\n",
       "      <td>224.82</td>\n",
       "      <td>3387187</td>\n",
       "      <td>20140729</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8.544335</td>\n",
       "      <td>7.967158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-30</th>\n",
       "      <td>228.92</td>\n",
       "      <td>229.60</td>\n",
       "      <td>221.04</td>\n",
       "      <td>1.74</td>\n",
       "      <td>221.92</td>\n",
       "      <td>225.01</td>\n",
       "      <td>4927823</td>\n",
       "      <td>20140730</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8.545081</td>\n",
       "      <td>8.009504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-31</th>\n",
       "      <td>223.30</td>\n",
       "      <td>231.40</td>\n",
       "      <td>221.50</td>\n",
       "      <td>-2.45</td>\n",
       "      <td>229.26</td>\n",
       "      <td>228.92</td>\n",
       "      <td>7749058</td>\n",
       "      <td>20140731</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>8.609601</td>\n",
       "      <td>8.144540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close    high     low  p_change    open  pre_close   volume  \\\n",
       "2014-07-23  222.49  224.75  219.43      1.33  220.01     219.58  3088731   \n",
       "2014-07-24  223.54  225.10  220.80      0.47  223.25     222.49  3248410   \n",
       "2014-07-25  223.57  226.97  221.75      0.01  222.72     223.54  3090383   \n",
       "2014-07-28  224.82  232.00  221.40      0.56  224.25     223.57  6517611   \n",
       "2014-07-29  225.01  228.30  224.86      0.08  226.61     224.82  3387187   \n",
       "2014-07-30  228.92  229.60  221.04      1.74  221.92     225.01  4927823   \n",
       "2014-07-31  223.30  231.40  221.50     -2.45  229.26     228.92  7749058   \n",
       "\n",
       "                date  date_week  key     atr21     atr14  \n",
       "2014-07-23  20140723          2    0  8.977539  8.459104  \n",
       "2014-07-24  20140724          3    1  8.812894  8.249168  \n",
       "2014-07-25  20140725          4    2  8.641804  8.032799  \n",
       "2014-07-28  20140728          0    3  8.735052  8.216171  \n",
       "2014-07-29  20140729          1    4  8.544335  7.967158  \n",
       "2014-07-30  20140730          2    5  8.545081  8.009504  \n",
       "2014-07-31  20140731          3    6  8.609601  8.144540  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2014-07-23至2014-07-31 所有序列，表4-9所示\n",
    "tsla_df.loc['2014-07-23':'2014-07-31']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-07-24</th>\n",
       "      <td>220.80</td>\n",
       "      <td>0.47</td>\n",
       "      <td>223.25</td>\n",
       "      <td>222.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-25</th>\n",
       "      <td>221.75</td>\n",
       "      <td>0.01</td>\n",
       "      <td>222.72</td>\n",
       "      <td>223.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-28</th>\n",
       "      <td>221.40</td>\n",
       "      <td>0.56</td>\n",
       "      <td>224.25</td>\n",
       "      <td>223.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-29</th>\n",
       "      <td>224.86</td>\n",
       "      <td>0.08</td>\n",
       "      <td>226.61</td>\n",
       "      <td>224.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               low  p_change    open  pre_close\n",
       "2014-07-24  220.80      0.47  223.25     222.49\n",
       "2014-07-25  221.75      0.01  222.72     223.54\n",
       "2014-07-28  221.40      0.56  224.25     223.57\n",
       "2014-07-29  224.86      0.08  226.61     224.82"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# [1:5]：(1，2，3，4)，[2:6]: (2, 3, 4, 5)\n",
    "# 表4-10所示\n",
    "tsla_df.iloc[1:5, 2:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-09-11</th>\n",
       "      <td>280.31</td>\n",
       "      <td>284.79</td>\n",
       "      <td>278.63</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>280.46</td>\n",
       "      <td>281.10</td>\n",
       "      <td>3768584</td>\n",
       "      <td>20140911</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>9.114488</td>\n",
       "      <td>9.125375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-12</th>\n",
       "      <td>279.20</td>\n",
       "      <td>282.39</td>\n",
       "      <td>277.00</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>280.50</td>\n",
       "      <td>280.31</td>\n",
       "      <td>3328302</td>\n",
       "      <td>20140912</td>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>8.937132</td>\n",
       "      <td>8.858562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close    high     low  p_change    open  pre_close   volume  \\\n",
       "2014-09-11  280.31  284.79  278.63     -0.28  280.46     281.10  3768584   \n",
       "2014-09-12  279.20  282.39  277.00     -0.40  280.50     280.31  3328302   \n",
       "\n",
       "                date  date_week  key     atr21     atr14  \n",
       "2014-09-11  20140911          3   35  9.114488  9.125375  \n",
       "2014-09-12  20140912          4   36  8.937132  8.858562  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 切取所有行[2:6]: (2, 3, 4, 5)列\n",
    "tsla_df.iloc[:, 2:6]\n",
    "# 选取所有的列[35:37]:(35, 36)行，表4-11所示\n",
    "tsla_df.iloc[35:37]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-07-23    222.49\n",
      "2014-07-24    223.54\n",
      "2014-07-25    223.57\n",
      "Name: close, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-07-23</th>\n",
       "      <td>222.49</td>\n",
       "      <td>224.75</td>\n",
       "      <td>219.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-24</th>\n",
       "      <td>223.54</td>\n",
       "      <td>225.10</td>\n",
       "      <td>220.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-25</th>\n",
       "      <td>223.57</td>\n",
       "      <td>226.97</td>\n",
       "      <td>221.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close    high     low\n",
       "2014-07-23  222.49  224.75  219.43\n",
       "2014-07-24  223.54  225.10  220.80\n",
       "2014-07-25  223.57  226.97  221.75"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 指定一个列\n",
    "print(tsla_df.close[0:3])\n",
    "# 通过组成一个列表选择多个列，表4-12所示\n",
    "tsla_df[['close', 'high', 'low']][0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2.3 逻辑条件进行数据筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-09-15</th>\n",
       "      <td>253.86</td>\n",
       "      <td>274.40</td>\n",
       "      <td>249.13</td>\n",
       "      <td>-9.08</td>\n",
       "      <td>274.370</td>\n",
       "      <td>279.20</td>\n",
       "      <td>16464949</td>\n",
       "      <td>20140915</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>9.996316</td>\n",
       "      <td>10.452951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>242.77</td>\n",
       "      <td>244.60</td>\n",
       "      <td>228.25</td>\n",
       "      <td>9.52</td>\n",
       "      <td>229.600</td>\n",
       "      <td>221.67</td>\n",
       "      <td>10516300</td>\n",
       "      <td>20141028</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>11.259016</td>\n",
       "      <td>11.439155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-06</th>\n",
       "      <td>246.13</td>\n",
       "      <td>255.00</td>\n",
       "      <td>236.12</td>\n",
       "      <td>-8.88</td>\n",
       "      <td>249.540</td>\n",
       "      <td>270.13</td>\n",
       "      <td>14623754</td>\n",
       "      <td>20150806</td>\n",
       "      <td>3</td>\n",
       "      <td>261</td>\n",
       "      <td>10.797217</td>\n",
       "      <td>11.594563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-27</th>\n",
       "      <td>242.99</td>\n",
       "      <td>244.75</td>\n",
       "      <td>230.81</td>\n",
       "      <td>8.07</td>\n",
       "      <td>231.000</td>\n",
       "      <td>224.84</td>\n",
       "      <td>7655959</td>\n",
       "      <td>20150827</td>\n",
       "      <td>3</td>\n",
       "      <td>276</td>\n",
       "      <td>14.347205</td>\n",
       "      <td>15.894958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-04</th>\n",
       "      <td>231.63</td>\n",
       "      <td>232.74</td>\n",
       "      <td>225.20</td>\n",
       "      <td>11.17</td>\n",
       "      <td>227.000</td>\n",
       "      <td>208.35</td>\n",
       "      <td>12726366</td>\n",
       "      <td>20151104</td>\n",
       "      <td>2</td>\n",
       "      <td>324</td>\n",
       "      <td>11.150358</td>\n",
       "      <td>10.872516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-08</th>\n",
       "      <td>147.99</td>\n",
       "      <td>157.15</td>\n",
       "      <td>146.00</td>\n",
       "      <td>-8.99</td>\n",
       "      <td>157.105</td>\n",
       "      <td>162.61</td>\n",
       "      <td>9312988</td>\n",
       "      <td>20160208</td>\n",
       "      <td>0</td>\n",
       "      <td>388</td>\n",
       "      <td>12.329239</td>\n",
       "      <td>13.355117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-17</th>\n",
       "      <td>168.68</td>\n",
       "      <td>169.34</td>\n",
       "      <td>156.68</td>\n",
       "      <td>8.71</td>\n",
       "      <td>159.000</td>\n",
       "      <td>155.17</td>\n",
       "      <td>5825159</td>\n",
       "      <td>20160217</td>\n",
       "      <td>2</td>\n",
       "      <td>394</td>\n",
       "      <td>13.205443</td>\n",
       "      <td>14.213429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-22</th>\n",
       "      <td>196.66</td>\n",
       "      <td>205.95</td>\n",
       "      <td>195.75</td>\n",
       "      <td>-10.45</td>\n",
       "      <td>199.470</td>\n",
       "      <td>219.61</td>\n",
       "      <td>23742414</td>\n",
       "      <td>20160622</td>\n",
       "      <td>2</td>\n",
       "      <td>481</td>\n",
       "      <td>9.890044</td>\n",
       "      <td>9.868338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close    high     low  p_change     open  pre_close    volume  \\\n",
       "2014-09-15  253.86  274.40  249.13     -9.08  274.370     279.20  16464949   \n",
       "2014-10-28  242.77  244.60  228.25      9.52  229.600     221.67  10516300   \n",
       "2015-08-06  246.13  255.00  236.12     -8.88  249.540     270.13  14623754   \n",
       "2015-08-27  242.99  244.75  230.81      8.07  231.000     224.84   7655959   \n",
       "2015-11-04  231.63  232.74  225.20     11.17  227.000     208.35  12726366   \n",
       "2016-02-08  147.99  157.15  146.00     -8.99  157.105     162.61   9312988   \n",
       "2016-02-17  168.68  169.34  156.68      8.71  159.000     155.17   5825159   \n",
       "2016-06-22  196.66  205.95  195.75    -10.45  199.470     219.61  23742414   \n",
       "\n",
       "                date  date_week  key      atr21      atr14  \n",
       "2014-09-15  20140915          0   37   9.996316  10.452951  \n",
       "2014-10-28  20141028          1   68  11.259016  11.439155  \n",
       "2015-08-06  20150806          3  261  10.797217  11.594563  \n",
       "2015-08-27  20150827          3  276  14.347205  15.894958  \n",
       "2015-11-04  20151104          2  324  11.150358  10.872516  \n",
       "2016-02-08  20160208          0  388  12.329239  13.355117  \n",
       "2016-02-17  20160217          2  394  13.205443  14.213429  \n",
       "2016-06-22  20160622          2  481   9.890044   9.868338  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# abs为取绝对值的意思，不是防抱死，表4-13所示\n",
    "tsla_df[np.abs(tsla_df.p_change) > 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-09-15</th>\n",
       "      <td>253.86</td>\n",
       "      <td>274.40</td>\n",
       "      <td>249.13</td>\n",
       "      <td>-9.08</td>\n",
       "      <td>274.37</td>\n",
       "      <td>279.20</td>\n",
       "      <td>16464949</td>\n",
       "      <td>20140915</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>9.996316</td>\n",
       "      <td>10.452951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-06</th>\n",
       "      <td>246.13</td>\n",
       "      <td>255.00</td>\n",
       "      <td>236.12</td>\n",
       "      <td>-8.88</td>\n",
       "      <td>249.54</td>\n",
       "      <td>270.13</td>\n",
       "      <td>14623754</td>\n",
       "      <td>20150806</td>\n",
       "      <td>3</td>\n",
       "      <td>261</td>\n",
       "      <td>10.797217</td>\n",
       "      <td>11.594563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-04</th>\n",
       "      <td>231.63</td>\n",
       "      <td>232.74</td>\n",
       "      <td>225.20</td>\n",
       "      <td>11.17</td>\n",
       "      <td>227.00</td>\n",
       "      <td>208.35</td>\n",
       "      <td>12726366</td>\n",
       "      <td>20151104</td>\n",
       "      <td>2</td>\n",
       "      <td>324</td>\n",
       "      <td>11.150358</td>\n",
       "      <td>10.872516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-22</th>\n",
       "      <td>196.66</td>\n",
       "      <td>205.95</td>\n",
       "      <td>195.75</td>\n",
       "      <td>-10.45</td>\n",
       "      <td>199.47</td>\n",
       "      <td>219.61</td>\n",
       "      <td>23742414</td>\n",
       "      <td>20160622</td>\n",
       "      <td>2</td>\n",
       "      <td>481</td>\n",
       "      <td>9.890044</td>\n",
       "      <td>9.868338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close    high     low  p_change    open  pre_close    volume  \\\n",
       "2014-09-15  253.86  274.40  249.13     -9.08  274.37     279.20  16464949   \n",
       "2015-08-06  246.13  255.00  236.12     -8.88  249.54     270.13  14623754   \n",
       "2015-11-04  231.63  232.74  225.20     11.17  227.00     208.35  12726366   \n",
       "2016-06-22  196.66  205.95  195.75    -10.45  199.47     219.61  23742414   \n",
       "\n",
       "                date  date_week  key      atr21      atr14  \n",
       "2014-09-15  20140915          0   37   9.996316  10.452951  \n",
       "2015-08-06  20150806          3  261  10.797217  11.594563  \n",
       "2015-11-04  20151104          2  324  11.150358  10.872516  \n",
       "2016-06-22  20160622          2  481   9.890044   9.868338  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsla_df[(np.abs(tsla_df.p_change) > 8) & (tsla_df.volume > 2.5 * tsla_df.volume.mean())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2.4 数据转换与规整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-06-22</th>\n",
       "      <td>196.66</td>\n",
       "      <td>205.95</td>\n",
       "      <td>195.75</td>\n",
       "      <td>-10.45</td>\n",
       "      <td>199.470</td>\n",
       "      <td>219.61</td>\n",
       "      <td>23742414</td>\n",
       "      <td>20160622</td>\n",
       "      <td>2</td>\n",
       "      <td>481</td>\n",
       "      <td>9.890044</td>\n",
       "      <td>9.868338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-15</th>\n",
       "      <td>253.86</td>\n",
       "      <td>274.40</td>\n",
       "      <td>249.13</td>\n",
       "      <td>-9.08</td>\n",
       "      <td>274.370</td>\n",
       "      <td>279.20</td>\n",
       "      <td>16464949</td>\n",
       "      <td>20140915</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>9.996316</td>\n",
       "      <td>10.452951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-08</th>\n",
       "      <td>147.99</td>\n",
       "      <td>157.15</td>\n",
       "      <td>146.00</td>\n",
       "      <td>-8.99</td>\n",
       "      <td>157.105</td>\n",
       "      <td>162.61</td>\n",
       "      <td>9312988</td>\n",
       "      <td>20160208</td>\n",
       "      <td>0</td>\n",
       "      <td>388</td>\n",
       "      <td>12.329239</td>\n",
       "      <td>13.355117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-06</th>\n",
       "      <td>246.13</td>\n",
       "      <td>255.00</td>\n",
       "      <td>236.12</td>\n",
       "      <td>-8.88</td>\n",
       "      <td>249.540</td>\n",
       "      <td>270.13</td>\n",
       "      <td>14623754</td>\n",
       "      <td>20150806</td>\n",
       "      <td>3</td>\n",
       "      <td>261</td>\n",
       "      <td>10.797217</td>\n",
       "      <td>11.594563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-10</th>\n",
       "      <td>236.91</td>\n",
       "      <td>245.89</td>\n",
       "      <td>235.20</td>\n",
       "      <td>-7.82</td>\n",
       "      <td>244.640</td>\n",
       "      <td>257.01</td>\n",
       "      <td>12898280</td>\n",
       "      <td>20141010</td>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "      <td>10.898735</td>\n",
       "      <td>11.333807</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close    high     low  p_change     open  pre_close    volume  \\\n",
       "2016-06-22  196.66  205.95  195.75    -10.45  199.470     219.61  23742414   \n",
       "2014-09-15  253.86  274.40  249.13     -9.08  274.370     279.20  16464949   \n",
       "2016-02-08  147.99  157.15  146.00     -8.99  157.105     162.61   9312988   \n",
       "2015-08-06  246.13  255.00  236.12     -8.88  249.540     270.13  14623754   \n",
       "2014-10-10  236.91  245.89  235.20     -7.82  244.640     257.01  12898280   \n",
       "\n",
       "                date  date_week  key      atr21      atr14  \n",
       "2016-06-22  20160622          2  481   9.890044   9.868338  \n",
       "2014-09-15  20140915          0   37   9.996316  10.452951  \n",
       "2016-02-08  20160208          0  388  12.329239  13.355117  \n",
       "2015-08-06  20150806          3  261  10.797217  11.594563  \n",
       "2014-10-10  20141010          4   56  10.898735  11.333807  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# deprecated sort_index use sort_values\n",
    "# tsla_df.sort_index(by='p_change')[:5]\n",
    "tsla_df.sort_values(by='p_change')[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11-04</th>\n",
       "      <td>231.63</td>\n",
       "      <td>232.74</td>\n",
       "      <td>225.20</td>\n",
       "      <td>11.17</td>\n",
       "      <td>227.00</td>\n",
       "      <td>208.35</td>\n",
       "      <td>12726366</td>\n",
       "      <td>20151104</td>\n",
       "      <td>2</td>\n",
       "      <td>324</td>\n",
       "      <td>11.150358</td>\n",
       "      <td>10.872516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>242.77</td>\n",
       "      <td>244.60</td>\n",
       "      <td>228.25</td>\n",
       "      <td>9.52</td>\n",
       "      <td>229.60</td>\n",
       "      <td>221.67</td>\n",
       "      <td>10516300</td>\n",
       "      <td>20141028</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>11.259016</td>\n",
       "      <td>11.439155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-17</th>\n",
       "      <td>168.68</td>\n",
       "      <td>169.34</td>\n",
       "      <td>156.68</td>\n",
       "      <td>8.71</td>\n",
       "      <td>159.00</td>\n",
       "      <td>155.17</td>\n",
       "      <td>5825159</td>\n",
       "      <td>20160217</td>\n",
       "      <td>2</td>\n",
       "      <td>394</td>\n",
       "      <td>13.205443</td>\n",
       "      <td>14.213429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-27</th>\n",
       "      <td>242.99</td>\n",
       "      <td>244.75</td>\n",
       "      <td>230.81</td>\n",
       "      <td>8.07</td>\n",
       "      <td>231.00</td>\n",
       "      <td>224.84</td>\n",
       "      <td>7655959</td>\n",
       "      <td>20150827</td>\n",
       "      <td>3</td>\n",
       "      <td>276</td>\n",
       "      <td>14.347205</td>\n",
       "      <td>15.894958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-22</th>\n",
       "      <td>177.74</td>\n",
       "      <td>178.91</td>\n",
       "      <td>169.85</td>\n",
       "      <td>6.70</td>\n",
       "      <td>170.12</td>\n",
       "      <td>166.58</td>\n",
       "      <td>5055340</td>\n",
       "      <td>20160222</td>\n",
       "      <td>0</td>\n",
       "      <td>397</td>\n",
       "      <td>13.033677</td>\n",
       "      <td>13.752159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close    high     low  p_change    open  pre_close    volume  \\\n",
       "2015-11-04  231.63  232.74  225.20     11.17  227.00     208.35  12726366   \n",
       "2014-10-28  242.77  244.60  228.25      9.52  229.60     221.67  10516300   \n",
       "2016-02-17  168.68  169.34  156.68      8.71  159.00     155.17   5825159   \n",
       "2015-08-27  242.99  244.75  230.81      8.07  231.00     224.84   7655959   \n",
       "2016-02-22  177.74  178.91  169.85      6.70  170.12     166.58   5055340   \n",
       "\n",
       "                date  date_week  key      atr21      atr14  \n",
       "2015-11-04  20151104          2  324  11.150358  10.872516  \n",
       "2014-10-28  20141028          1   68  11.259016  11.439155  \n",
       "2016-02-17  20160217          2  394  13.205443  14.213429  \n",
       "2015-08-27  20150827          3  276  14.347205  15.894958  \n",
       "2016-02-22  20160222          0  397  13.033677  13.752159  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# deprecated sort_index use sort_values\n",
    "# tsla_df.sort_index(by='p_change', ascending=False)[:5]\n",
    "tsla_df.sort_values(by='p_change', ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-07-23</th>\n",
       "      <td>222.49</td>\n",
       "      <td>224.75</td>\n",
       "      <td>219.43</td>\n",
       "      <td>1.33</td>\n",
       "      <td>220.01</td>\n",
       "      <td>219.58</td>\n",
       "      <td>3088731</td>\n",
       "      <td>20140723</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8.977539</td>\n",
       "      <td>8.459104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-24</th>\n",
       "      <td>223.54</td>\n",
       "      <td>225.10</td>\n",
       "      <td>220.80</td>\n",
       "      <td>0.47</td>\n",
       "      <td>223.25</td>\n",
       "      <td>222.49</td>\n",
       "      <td>3248410</td>\n",
       "      <td>20140724</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8.812894</td>\n",
       "      <td>8.249168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-25</th>\n",
       "      <td>223.57</td>\n",
       "      <td>226.97</td>\n",
       "      <td>221.75</td>\n",
       "      <td>0.01</td>\n",
       "      <td>222.72</td>\n",
       "      <td>223.54</td>\n",
       "      <td>3090383</td>\n",
       "      <td>20140725</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8.641804</td>\n",
       "      <td>8.032799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-28</th>\n",
       "      <td>224.82</td>\n",
       "      <td>232.00</td>\n",
       "      <td>221.40</td>\n",
       "      <td>0.56</td>\n",
       "      <td>224.25</td>\n",
       "      <td>223.57</td>\n",
       "      <td>6517611</td>\n",
       "      <td>20140728</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.735052</td>\n",
       "      <td>8.216171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-29</th>\n",
       "      <td>225.01</td>\n",
       "      <td>228.30</td>\n",
       "      <td>224.86</td>\n",
       "      <td>0.08</td>\n",
       "      <td>226.61</td>\n",
       "      <td>224.82</td>\n",
       "      <td>3387187</td>\n",
       "      <td>20140729</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8.544335</td>\n",
       "      <td>7.967158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close    high     low  p_change    open  pre_close   volume  \\\n",
       "2014-07-23  222.49  224.75  219.43      1.33  220.01     219.58  3088731   \n",
       "2014-07-24  223.54  225.10  220.80      0.47  223.25     222.49  3248410   \n",
       "2014-07-25  223.57  226.97  221.75      0.01  222.72     223.54  3090383   \n",
       "2014-07-28  224.82  232.00  221.40      0.56  224.25     223.57  6517611   \n",
       "2014-07-29  225.01  228.30  224.86      0.08  226.61     224.82  3387187   \n",
       "\n",
       "                date  date_week  key     atr21     atr14  \n",
       "2014-07-23  20140723          2    0  8.977539  8.459104  \n",
       "2014-07-24  20140724          3    1  8.812894  8.249168  \n",
       "2014-07-25  20140725          4    2  8.641804  8.032799  \n",
       "2014-07-28  20140728          0    3  8.735052  8.216171  \n",
       "2014-07-29  20140729          1    4  8.544335  7.967158  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果一行的数据中存在na就删除这行\n",
    "tsla_df.dropna()            \n",
    "# 通过how控制 如果一行的数据中全部都是na就删除这行\n",
    "tsla_df.dropna(how='all')    \n",
    "\n",
    "# 使用指定值填充na， inplace代表就地操作，即不返回新的序列在原始序列上修改\n",
    "tsla_df.fillna(tsla_df.mean(), inplace=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-07-23    222.49\n",
       "2014-07-24    223.54\n",
       "2014-07-25    223.57\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsla_df.close[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-07-23         NaN\n",
       "2014-07-24    0.004719\n",
       "2014-07-25    0.000134\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsla_df.close.pct_change()[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.004719313227560713, 0.00013420416927619727)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(223.54 - 222.49) / 222.49, (223.57 - 223.54) / 223.54"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-07-20    0.013762\n",
       "2016-07-21   -0.034419\n",
       "2016-07-22    0.008027\n",
       "2016-07-25    0.034823\n",
       "2016-07-26   -0.017738\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pct_change对序列从第二项开始向前做减法在除以前一项，这样的针对close做pct_change后的结果就是涨跌幅\n",
    "change_ratio = tsla_df.close.pct_change()\n",
    "change_ratio.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-07-20    1.38\n",
       "2016-07-21   -3.44\n",
       "2016-07-22    0.80\n",
       "2016-07-25    3.48\n",
       "2016-07-26   -1.77\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将change_ratio转变成与tsla_df.p_change字段一样的百分百，同样保留两位小数\n",
    "np.round(change_ratio[-5:] * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-07-20    9.19\n",
       "2016-07-21    9.17\n",
       "2016-07-22    9.19\n",
       "2016-07-25    9.27\n",
       "2016-07-26    9.13\n",
       "Name: atr21, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format = lambda x: '%.2f' % x\n",
    "tsla_df.atr21.map(format).tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2.5 数据本地序列化操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tsla_df.to_csv('../gen/tsla_df.csv', columns=tsla_df.columns, index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-07-23</th>\n",
       "      <td>222.49</td>\n",
       "      <td>224.75</td>\n",
       "      <td>219.43</td>\n",
       "      <td>1.33</td>\n",
       "      <td>220.01</td>\n",
       "      <td>219.58</td>\n",
       "      <td>3088731</td>\n",
       "      <td>20140723</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8.932286</td>\n",
       "      <td>10.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-24</th>\n",
       "      <td>223.54</td>\n",
       "      <td>225.10</td>\n",
       "      <td>220.80</td>\n",
       "      <td>0.47</td>\n",
       "      <td>223.25</td>\n",
       "      <td>222.49</td>\n",
       "      <td>3248410</td>\n",
       "      <td>20140724</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8.932286</td>\n",
       "      <td>10.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-25</th>\n",
       "      <td>223.57</td>\n",
       "      <td>226.97</td>\n",
       "      <td>221.75</td>\n",
       "      <td>0.01</td>\n",
       "      <td>222.72</td>\n",
       "      <td>223.54</td>\n",
       "      <td>3090383</td>\n",
       "      <td>20140725</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8.932286</td>\n",
       "      <td>10.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-28</th>\n",
       "      <td>224.82</td>\n",
       "      <td>232.00</td>\n",
       "      <td>221.40</td>\n",
       "      <td>0.56</td>\n",
       "      <td>224.25</td>\n",
       "      <td>223.57</td>\n",
       "      <td>6517611</td>\n",
       "      <td>20140728</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.932286</td>\n",
       "      <td>10.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-29</th>\n",
       "      <td>225.01</td>\n",
       "      <td>228.30</td>\n",
       "      <td>224.86</td>\n",
       "      <td>0.08</td>\n",
       "      <td>226.61</td>\n",
       "      <td>224.82</td>\n",
       "      <td>3387187</td>\n",
       "      <td>20140729</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8.932286</td>\n",
       "      <td>10.202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close    high     low  p_change    open  pre_close   volume  \\\n",
       "2014-07-23  222.49  224.75  219.43      1.33  220.01     219.58  3088731   \n",
       "2014-07-24  223.54  225.10  220.80      0.47  223.25     222.49  3248410   \n",
       "2014-07-25  223.57  226.97  221.75      0.01  222.72     223.54  3090383   \n",
       "2014-07-28  224.82  232.00  221.40      0.56  224.25     223.57  6517611   \n",
       "2014-07-29  225.01  228.30  224.86      0.08  226.61     224.82  3387187   \n",
       "\n",
       "                date  date_week  key     atr21   atr14  \n",
       "2014-07-23  20140723          2    0  8.932286  10.202  \n",
       "2014-07-24  20140724          3    1  8.932286  10.202  \n",
       "2014-07-25  20140725          4    2  8.932286  10.202  \n",
       "2014-07-28  20140728          0    3  8.932286  10.202  \n",
       "2014-07-29  20140729          1    4  8.932286  10.202  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsla_df_load = pd.read_csv('../gen/tsla_df.csv', parse_dates=True, index_col=0)\n",
    "tsla_df_load.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 实例1：寻找股票异动涨跌幅阀值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yoTHGoar6iSQPJtlK8mSSO8YYz+3ciAAAwCqbutNSVe9Ocn+SayaH7k1y9xjjdUnWkty2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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a6b0208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsla_df.p_change.hist(bins=80);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3.1 数据的离散化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.221, 11.17]    51\n",
       "(2.324, 3.01]     51\n",
       "(1.764, 2.324]    51\n",
       "(0.72, 1.04]      51\n",
       "(0.436, 0.72]     51\n",
       "[0.01, 0.192]     51\n",
       "(1.34, 1.764]     50\n",
       "(0.192, 0.436]    50\n",
       "(3.01, 4.221]     49\n",
       "(1.04, 1.34]      49\n",
       "Name: p_change, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats = pd.qcut(np.abs(tsla_df.p_change), 10)\n",
    "cats.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 3]        209\n",
       "(-3, 0]       193\n",
       "(3, 5]         38\n",
       "(-5, -3]       35\n",
       "(5, 7]          9\n",
       "(-7, -5]        9\n",
       "(-inf, -7]      7\n",
       "(7, inf]        4\n",
       "Name: p_change, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将涨跌幅数据手工分类，从负无穷到－7，－5，－3，0， 3， 5， 7，正无穷\n",
    "bins = [-np.inf, -7.0, -5, -3, 0, 3, 5, 7, np.inf]\n",
    "cats = pd.cut(tsla_df.p_change, bins)\n",
    "cats.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cr_dummies_(-inf, -7]</th>\n",
       "      <th>cr_dummies_(-7, -5]</th>\n",
       "      <th>cr_dummies_(-5, -3]</th>\n",
       "      <th>cr_dummies_(-3, 0]</th>\n",
       "      <th>cr_dummies_(0, 3]</th>\n",
       "      <th>cr_dummies_(3, 5]</th>\n",
       "      <th>cr_dummies_(5, 7]</th>\n",
       "      <th>cr_dummies_(7, inf]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-07-23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            cr_dummies_(-inf, -7]  cr_dummies_(-7, -5]  cr_dummies_(-5, -3]  \\\n",
       "2014-07-23                      0                    0                    0   \n",
       "2014-07-24                      0                    0                    0   \n",
       "2014-07-25                      0                    0                    0   \n",
       "2014-07-28                      0                    0                    0   \n",
       "2014-07-29                      0                    0                    0   \n",
       "\n",
       "            cr_dummies_(-3, 0]  cr_dummies_(0, 3]  cr_dummies_(3, 5]  \\\n",
       "2014-07-23                   0                  1                  0   \n",
       "2014-07-24                   0                  1                  0   \n",
       "2014-07-25                   0                  1                  0   \n",
       "2014-07-28                   0                  1                  0   \n",
       "2014-07-29                   0                  1                  0   \n",
       "\n",
       "            cr_dummies_(5, 7]  cr_dummies_(7, inf]  \n",
       "2014-07-23                  0                    0  \n",
       "2014-07-24                  0                    0  \n",
       "2014-07-25                  0                    0  \n",
       "2014-07-28                  0                    0  \n",
       "2014-07-29                  0                    0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cr_dummies为列名称前缀\n",
    "change_ration_dummies = pd.get_dummies(cats, prefix='cr_dummies')\n",
    "change_ration_dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3.2 concat, append, merge的使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>...</th>\n",
       "      <th>cr_dummies_(-5, -3]</th>\n",
       "      <th>cr_dummies_(-3, 0]</th>\n",
       "      <th>cr_dummies_(0, 3]</th>\n",
       "      <th>cr_dummies_(3, 5]</th>\n",
       "      <th>cr_dummies_(5, 7]</th>\n",
       "      <th>cr_dummies_(7, inf]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-07-20</th>\n",
       "      <td>228.36</td>\n",
       "      <td>229.800</td>\n",
       "      <td>225.00</td>\n",
       "      <td>1.38</td>\n",
       "      <td>226.47</td>\n",
       "      <td>225.26</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-21</th>\n",
       "      <td>220.50</td>\n",
       "      <td>227.847</td>\n",
       "      <td>219.10</td>\n",
       "      <td>-3.44</td>\n",
       "      <td>226.00</td>\n",
       "      <td>228.36</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-22</th>\n",
       "      <td>222.27</td>\n",
       "      <td>224.500</td>\n",
       "      <td>218.88</td>\n",
       "      <td>0.80</td>\n",
       "      <td>221.99</td>\n",
       "      <td>220.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-25</th>\n",
       "      <td>230.01</td>\n",
       "      <td>231.390</td>\n",
       "      <td>221.37</td>\n",
       "      <td>3.48</td>\n",
       "      <td>222.27</td>\n",
       "      <td>222.27</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-26</th>\n",
       "      <td>225.93</td>\n",
       "      <td>228.740</td>\n",
       "      <td>225.63</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>227.34</td>\n",
       "      <td>230.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             close     high     low  p_change    open  pre_close  \\\n",
       "2016-07-20  228.36  229.800  225.00      1.38  226.47     225.26   \n",
       "2016-07-21  220.50  227.847  219.10     -3.44  226.00     228.36   \n",
       "2016-07-22  222.27  224.500  218.88      0.80  221.99     220.50   \n",
       "2016-07-25  230.01  231.390  221.37      3.48  222.27     222.27   \n",
       "2016-07-26  225.93  228.740  225.63     -1.77  227.34     230.01   \n",
       "\n",
       "                   ...           cr_dummies_(-5, -3]  cr_dummies_(-3, 0]  \\\n",
       "2016-07-20         ...                             0                   0   \n",
       "2016-07-21         ...                             1                   0   \n",
       "2016-07-22         ...                             0                   0   \n",
       "2016-07-25         ...                             0                   0   \n",
       "2016-07-26         ...                             0                   1   \n",
       "\n",
       "            cr_dummies_(0, 3]  cr_dummies_(3, 5]  cr_dummies_(5, 7]  \\\n",
       "2016-07-20                  1                  0                  0   \n",
       "2016-07-21                  0                  0                  0   \n",
       "2016-07-22                  1                  0                  0   \n",
       "2016-07-25                  0                  1                  0   \n",
       "2016-07-26                  0                  0                  0   \n",
       "\n",
       "            cr_dummies_(7, inf]  \n",
       "2016-07-20                    0  \n",
       "2016-07-21                    0  \n",
       "2016-07-22                    0  \n",
       "2016-07-25                    0  \n",
       "2016-07-26                    0  \n",
       "\n",
       "[5 rows x 20 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([tsla_df, change_ration_dummies], axis=1).tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11-04</th>\n",
       "      <td>231.63</td>\n",
       "      <td>232.74</td>\n",
       "      <td>225.20</td>\n",
       "      <td>11.17</td>\n",
       "      <td>227.00</td>\n",
       "      <td>208.35</td>\n",
       "      <td>12726366</td>\n",
       "      <td>20151104</td>\n",
       "      <td>2</td>\n",
       "      <td>324</td>\n",
       "      <td>11.150358</td>\n",
       "      <td>10.872516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-28</th>\n",
       "      <td>248.48</td>\n",
       "      <td>251.45</td>\n",
       "      <td>241.57</td>\n",
       "      <td>2.26</td>\n",
       "      <td>241.86</td>\n",
       "      <td>242.99</td>\n",
       "      <td>5513673</td>\n",
       "      <td>20150828</td>\n",
       "      <td>4</td>\n",
       "      <td>277</td>\n",
       "      <td>14.931148</td>\n",
       "      <td>16.660318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>249.06</td>\n",
       "      <td>254.95</td>\n",
       "      <td>245.51</td>\n",
       "      <td>0.23</td>\n",
       "      <td>245.62</td>\n",
       "      <td>248.48</td>\n",
       "      <td>4700232</td>\n",
       "      <td>20150831</td>\n",
       "      <td>0</td>\n",
       "      <td>278</td>\n",
       "      <td>14.789664</td>\n",
       "      <td>16.324581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close    high     low  p_change    open  pre_close    volume  \\\n",
       "2015-11-04  231.63  232.74  225.20     11.17  227.00     208.35  12726366   \n",
       "2015-08-28  248.48  251.45  241.57      2.26  241.86     242.99   5513673   \n",
       "2015-08-31  249.06  254.95  245.51      0.23  245.62     248.48   4700232   \n",
       "\n",
       "                date  date_week  key      atr21      atr14  \n",
       "2015-11-04  20151104          2  324  11.150358  10.872516  \n",
       "2015-08-28  20150828          4  277  14.931148  16.660318  \n",
       "2015-08-31  20150831          0  278  14.789664  16.324581  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd.concat的连接axis＝0：纵向连接atr>14的df和p_change > 10的df\n",
    "pd.concat([tsla_df[tsla_df.p_change > 10],\n",
    "           tsla_df[tsla_df.atr14 > 16]], axis=0)\n",
    "\n",
    "# 直接使用DataFrame对象append，结果与上面pd.concat的结果一致, 表4-20所示\n",
    "tsla_df[tsla_df.p_change > 10].append(\n",
    "    tsla_df[tsla_df.atr14 > 16])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>stock_a</th>\n",
       "      <th>data2</th>\n",
       "      <th>stock_b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>c</td>\n",
       "      <td>2</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data stock_a  data2 stock_b\n",
       "0     0       a      0       a\n",
       "1     4       a      0       a\n",
       "2     1       b      1       b\n",
       "3     2       c      2       c"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_a = pd.DataFrame({'stock_a': ['a', 'b', 'c', 'd', 'a'],\n",
    "                 'data': list(range(5))})\n",
    "\n",
    "stock_b = pd.DataFrame({'stock_b': ['a', 'b', 'c'],\n",
    "                 'data2': list(range(3))})\n",
    "pd.merge(stock_a, stock_b, left_on='stock_a', right_on='stock_b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 实例2 ：星期几是这个股票的‘好日子’"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>...</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "      <th>positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-07-20</th>\n",
       "      <td>228.36</td>\n",
       "      <td>229.800</td>\n",
       "      <td>225.00</td>\n",
       "      <td>1.38</td>\n",
       "      <td>226.47</td>\n",
       "      <td>225.26</td>\n",
       "      <td>...</td>\n",
       "      <td>20160720</td>\n",
       "      <td>2</td>\n",
       "      <td>499</td>\n",
       "      <td>9.192273</td>\n",
       "      <td>8.723406</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-21</th>\n",
       "      <td>220.50</td>\n",
       "      <td>227.847</td>\n",
       "      <td>219.10</td>\n",
       "      <td>-3.44</td>\n",
       "      <td>226.00</td>\n",
       "      <td>228.36</td>\n",
       "      <td>...</td>\n",
       "      <td>20160721</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "      <td>9.171070</td>\n",
       "      <td>8.725091</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-22</th>\n",
       "      <td>222.27</td>\n",
       "      <td>224.500</td>\n",
       "      <td>218.88</td>\n",
       "      <td>0.80</td>\n",
       "      <td>221.99</td>\n",
       "      <td>220.50</td>\n",
       "      <td>...</td>\n",
       "      <td>20160722</td>\n",
       "      <td>4</td>\n",
       "      <td>501</td>\n",
       "      <td>9.185781</td>\n",
       "      <td>8.779013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-25</th>\n",
       "      <td>230.01</td>\n",
       "      <td>231.390</td>\n",
       "      <td>221.37</td>\n",
       "      <td>3.48</td>\n",
       "      <td>222.27</td>\n",
       "      <td>222.27</td>\n",
       "      <td>...</td>\n",
       "      <td>20160725</td>\n",
       "      <td>0</td>\n",
       "      <td>502</td>\n",
       "      <td>9.266934</td>\n",
       "      <td>8.929798</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-26</th>\n",
       "      <td>225.93</td>\n",
       "      <td>228.740</td>\n",
       "      <td>225.63</td>\n",
       "      <td>-1.77</td>\n",
       "      <td>227.34</td>\n",
       "      <td>230.01</td>\n",
       "      <td>...</td>\n",
       "      <td>20160726</td>\n",
       "      <td>1</td>\n",
       "      <td>503</td>\n",
       "      <td>9.133747</td>\n",
       "      <td>8.754098</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             close     high     low  p_change    open  pre_close    ...     \\\n",
       "2016-07-20  228.36  229.800  225.00      1.38  226.47     225.26    ...      \n",
       "2016-07-21  220.50  227.847  219.10     -3.44  226.00     228.36    ...      \n",
       "2016-07-22  222.27  224.500  218.88      0.80  221.99     220.50    ...      \n",
       "2016-07-25  230.01  231.390  221.37      3.48  222.27     222.27    ...      \n",
       "2016-07-26  225.93  228.740  225.63     -1.77  227.34     230.01    ...      \n",
       "\n",
       "                date  date_week  key     atr21     atr14  positive  \n",
       "2016-07-20  20160720          2  499  9.192273  8.723406         1  \n",
       "2016-07-21  20160721          3  500  9.171070  8.725091         0  \n",
       "2016-07-22  20160722          4  501  9.185781  8.779013         1  \n",
       "2016-07-25  20160725          0  502  9.266934  8.929798         1  \n",
       "2016-07-26  20160726          1  503  9.133747  8.754098         0  \n",
       "\n",
       "[5 rows x 13 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsla_df['positive'] = np.where(tsla_df.p_change > 0, 1, 0)\n",
    "tsla_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4.1  构建交叉表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>positive</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "positive    0   1\n",
       "date_week        \n",
       "0          44  51\n",
       "1          55  48\n",
       "2          48  57\n",
       "3          44  57\n",
       "4          53  47"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xt = pd.crosstab(tsla_df.date_week, tsla_df.positive)\n",
    "xt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>positive</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.463158</td>\n",
       "      <td>0.536842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.533981</td>\n",
       "      <td>0.466019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.457143</td>\n",
       "      <td>0.542857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.435644</td>\n",
       "      <td>0.564356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.470000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "positive          0         1\n",
       "date_week                    \n",
       "0          0.463158  0.536842\n",
       "1          0.533981  0.466019\n",
       "2          0.457143  0.542857\n",
       "3          0.435644  0.564356\n",
       "4          0.530000  0.470000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xt_pct = xt.div(xt.sum(1).astype(float), axis=0)\n",
    "xt_pct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11ac3c2e8>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HgMMj4hpg9czMBmuRJKljNDZtDkwAHmtZXhQRPZnZC6wJ7AC8H5gDXBIRN2fmVQM1NmnS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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aaeb9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xt_pct.plot(\n",
    "    figsize=(8, 5),\n",
    "    kind='bar',\n",
    "    stacked=True,\n",
    "    title='date_week -> positive')\n",
    "plt.xlabel('date_week')\n",
    "plt.ylabel('positive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4.2  构建透视表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_week</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.536842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.466019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.542857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.564356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.470000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           positive\n",
       "date_week          \n",
       "0          0.536842\n",
       "1          0.466019\n",
       "2          0.542857\n",
       "3          0.564356\n",
       "4          0.470000"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsla_df.pivot_table(['positive'], index=['date_week'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_week  positive\n",
       "0          0           44\n",
       "           1           51\n",
       "1          0           55\n",
       "           1           48\n",
       "2          0           48\n",
       "           1           57\n",
       "3          0           44\n",
       "           1           57\n",
       "4          0           53\n",
       "           1           47\n",
       "Name: positive, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsla_df.groupby(['date_week', 'positive'])['positive'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.5 实例3 ：跳空缺口"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.82845"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jump_threshold = tsla_df.close.median() * 0.03\n",
    "jump_threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>jump</th>\n",
       "      <th>jump_power</th>\n",
       "      <th>close</th>\n",
       "      <th>date</th>\n",
       "      <th>p_change</th>\n",
       "      <th>pre_close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-08-11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.006085</td>\n",
       "      <td>259.32</td>\n",
       "      <td>20140811.0</td>\n",
       "      <td>4.51</td>\n",
       "      <td>248.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-10</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.628481</td>\n",
       "      <td>236.91</td>\n",
       "      <td>20141010.0</td>\n",
       "      <td>-7.82</td>\n",
       "      <td>257.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-14</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.325337</td>\n",
       "      <td>192.69</td>\n",
       "      <td>20150114.0</td>\n",
       "      <td>-5.66</td>\n",
       "      <td>204.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-12</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.422285</td>\n",
       "      <td>202.88</td>\n",
       "      <td>20150212.0</td>\n",
       "      <td>-4.66</td>\n",
       "      <td>212.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-08</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.036839</td>\n",
       "      <td>254.96</td>\n",
       "      <td>20150708.0</td>\n",
       "      <td>-4.82</td>\n",
       "      <td>267.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-21</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.282868</td>\n",
       "      <td>266.77</td>\n",
       "      <td>20150721.0</td>\n",
       "      <td>-5.49</td>\n",
       "      <td>282.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-06</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.215730</td>\n",
       "      <td>246.13</td>\n",
       "      <td>20150806.0</td>\n",
       "      <td>-8.88</td>\n",
       "      <td>270.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.077843</td>\n",
       "      <td>254.99</td>\n",
       "      <td>20150817.0</td>\n",
       "      <td>4.87</td>\n",
       "      <td>243.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-04</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.467617</td>\n",
       "      <td>231.63</td>\n",
       "      <td>20151104.0</td>\n",
       "      <td>11.17</td>\n",
       "      <td>208.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.263830</td>\n",
       "      <td>223.41</td>\n",
       "      <td>20160104.0</td>\n",
       "      <td>-6.92</td>\n",
       "      <td>240.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-22</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.000454</td>\n",
       "      <td>196.66</td>\n",
       "      <td>20160622.0</td>\n",
       "      <td>-10.45</td>\n",
       "      <td>219.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            jump  jump_power   close        date  p_change  pre_close\n",
       "2014-08-11   1.0    1.006085  259.32  20140811.0      4.51     248.13\n",
       "2014-10-10  -1.0    1.628481  236.91  20141010.0     -7.82     257.01\n",
       "2015-01-14  -1.0    1.325337  192.69  20150114.0     -5.66     204.25\n",
       "2015-02-12  -1.0    1.422285  202.88  20150212.0     -4.66     212.80\n",
       "2015-07-08  -1.0    1.036839  254.96  20150708.0     -4.82     267.88\n",
       "2015-07-21  -1.0    1.282868  266.77  20150721.0     -5.49     282.26\n",
       "2015-08-06  -1.0    2.215730  246.13  20150806.0     -8.88     270.13\n",
       "2015-08-17   1.0    1.077843  254.99  20150817.0      4.87     243.15\n",
       "2015-11-04   1.0    2.467617  231.63  20151104.0     11.17     208.35\n",
       "2016-01-04  -1.0    1.263830  223.41  20160104.0     -6.92     240.01\n",
       "2016-06-22  -1.0    2.000454  196.66  20160622.0    -10.45     219.61"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jump_pd = pd.DataFrame()\n",
    "\n",
    "def judge_jump(p_today):\n",
    "    global jump_pd\n",
    "    if p_today.p_change > 0 and (p_today.low - p_today.pre_close) > jump_threshold:\n",
    "        \"\"\"\n",
    "            符合向上跳空\n",
    "        \"\"\"\n",
    "        # jump记录方向 1向上\n",
    "        p_today['jump'] = 1\n",
    "        # 向上跳能量＝（今天最低 － 昨收）／ 跳空阀值\n",
    "        p_today['jump_power'] = (p_today.low - p_today.pre_close) / jump_threshold\n",
    "        jump_pd = jump_pd.append(p_today)\n",
    "    elif p_today.p_change < 0 and (p_today.pre_close - p_today.high) > jump_threshold:\n",
    "        \"\"\"\n",
    "            符合向下跳空\n",
    "        \"\"\"\n",
    "        # jump记录方向 －1向下\n",
    "        p_today['jump'] = -1\n",
    "        # 向下跳能量＝（昨收 － 今天最高）／ 跳空阀值\n",
    "        p_today['jump_power'] = (p_today.pre_close - p_today.high) / jump_threshold\n",
    "        jump_pd = jump_pd.append(p_today)\n",
    "\n",
    "for kl_index in np.arange(0, tsla_df.shape[0]):\n",
    "    # 通过ix一个一个拿\n",
    "    today = tsla_df.ix[kl_index]\n",
    "    judge_jump(today)\n",
    "\n",
    "# filter按照顺序只显示这些列, 表4-26所示\n",
    "jump_pd.filter(['jump', 'jump_power', 'close', 'date', 'p_change', 'pre_close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atr14</th>\n",
       "      <th>atr21</th>\n",
       "      <th>close</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>high</th>\n",
       "      <th>...</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>p_change</th>\n",
       "      <th>positive</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-08-11</th>\n",
       "      <td>9.757172</td>\n",
       "      <td>9.645537</td>\n",
       "      <td>259.32</td>\n",
       "      <td>20140811.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>263.740</td>\n",
       "      <td>...</td>\n",
       "      <td>255.00</td>\n",
       "      <td>255.48</td>\n",
       "      <td>4.51</td>\n",
       "      <td>1.0</td>\n",
       "      <td>248.13</td>\n",
       "      <td>8101276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-11</th>\n",
       "      <td>9.757172</td>\n",
       "      <td>9.645537</td>\n",
       "      <td>259.32</td>\n",
       "      <td>20140811.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>263.740</td>\n",
       "      <td>...</td>\n",
       "      <td>255.00</td>\n",
       "      <td>255.48</td>\n",
       "      <td>4.51</td>\n",
       "      <td>1.0</td>\n",
       "      <td>248.13</td>\n",
       "      <td>8101276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-10</th>\n",
       "      <td>11.333807</td>\n",
       "      <td>10.898735</td>\n",
       "      <td>236.91</td>\n",
       "      <td>20141010.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>245.890</td>\n",
       "      <td>...</td>\n",
       "      <td>235.20</td>\n",
       "      <td>244.64</td>\n",
       "      <td>-7.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>257.01</td>\n",
       "      <td>12898280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-14</th>\n",
       "      <td>9.421497</td>\n",
       "      <td>9.550873</td>\n",
       "      <td>192.69</td>\n",
       "      <td>20150114.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195.200</td>\n",
       "      <td>...</td>\n",
       "      <td>185.00</td>\n",
       "      <td>185.83</td>\n",
       "      <td>-5.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>204.25</td>\n",
       "      <td>11551860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-12</th>\n",
       "      <td>9.973965</td>\n",
       "      <td>9.781666</td>\n",
       "      <td>202.88</td>\n",
       "      <td>20150212.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>203.088</td>\n",
       "      <td>...</td>\n",
       "      <td>193.28</td>\n",
       "      <td>193.57</td>\n",
       "      <td>-4.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>212.80</td>\n",
       "      <td>15649610.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-08</th>\n",
       "      <td>10.057524</td>\n",
       "      <td>9.208425</td>\n",
       "      <td>254.96</td>\n",
       "      <td>20150708.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>260.800</td>\n",
       "      <td>...</td>\n",
       "      <td>254.31</td>\n",
       "      <td>259.32</td>\n",
       "      <td>-4.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>267.88</td>\n",
       "      <td>6221077.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-21</th>\n",
       "      <td>9.982710</td>\n",
       "      <td>9.400292</td>\n",
       "      <td>266.77</td>\n",
       "      <td>20150721.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.500</td>\n",
       "      <td>...</td>\n",
       "      <td>266.55</td>\n",
       "      <td>270.05</td>\n",
       "      <td>-5.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>282.26</td>\n",
       "      <td>6108686.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-06</th>\n",
       "      <td>11.594563</td>\n",
       "      <td>10.797217</td>\n",
       "      <td>246.13</td>\n",
       "      <td>20150806.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>255.000</td>\n",
       "      <td>...</td>\n",
       "      <td>236.12</td>\n",
       "      <td>249.54</td>\n",
       "      <td>-8.88</td>\n",
       "      <td>0.0</td>\n",
       "      <td>270.13</td>\n",
       "      <td>14623754.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-17</th>\n",
       "      <td>11.975285</td>\n",
       "      <td>11.345597</td>\n",
       "      <td>254.99</td>\n",
       "      <td>20150817.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>256.590</td>\n",
       "      <td>...</td>\n",
       "      <td>250.51</td>\n",
       "      <td>255.56</td>\n",
       "      <td>4.87</td>\n",
       "      <td>1.0</td>\n",
       "      <td>243.15</td>\n",
       "      <td>7176690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-04</th>\n",
       "      <td>10.872516</td>\n",
       "      <td>11.150358</td>\n",
       "      <td>231.63</td>\n",
       "      <td>20151104.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>232.740</td>\n",
       "      <td>...</td>\n",
       "      <td>225.20</td>\n",
       "      <td>227.00</td>\n",
       "      <td>11.17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>208.35</td>\n",
       "      <td>12726366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>9.412426</td>\n",
       "      <td>9.567214</td>\n",
       "      <td>223.41</td>\n",
       "      <td>20160104.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>231.380</td>\n",
       "      <td>...</td>\n",
       "      <td>219.00</td>\n",
       "      <td>230.72</td>\n",
       "      <td>-6.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>240.01</td>\n",
       "      <td>6827146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-22</th>\n",
       "      <td>9.868338</td>\n",
       "      <td>9.890044</td>\n",
       "      <td>196.66</td>\n",
       "      <td>20160622.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>205.950</td>\n",
       "      <td>...</td>\n",
       "      <td>195.75</td>\n",
       "      <td>199.47</td>\n",
       "      <td>-10.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>219.61</td>\n",
       "      <td>23742414.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                atr14      atr21   close        date  date_week     high  \\\n",
       "2014-08-11   9.757172   9.645537  259.32  20140811.0        0.0  263.740   \n",
       "2014-08-11   9.757172   9.645537  259.32  20140811.0        0.0  263.740   \n",
       "2014-10-10  11.333807  10.898735  236.91  20141010.0        4.0  245.890   \n",
       "2015-01-14   9.421497   9.550873  192.69  20150114.0        2.0  195.200   \n",
       "2015-02-12   9.973965   9.781666  202.88  20150212.0        3.0  203.088   \n",
       "2015-07-08  10.057524   9.208425  254.96  20150708.0        2.0  260.800   \n",
       "2015-07-21   9.982710   9.400292  266.77  20150721.0        1.0  273.500   \n",
       "2015-08-06  11.594563  10.797217  246.13  20150806.0        3.0  255.000   \n",
       "2015-08-17  11.975285  11.345597  254.99  20150817.0        0.0  256.590   \n",
       "2015-11-04  10.872516  11.150358  231.63  20151104.0        2.0  232.740   \n",
       "2016-01-04   9.412426   9.567214  223.41  20160104.0        0.0  231.380   \n",
       "2016-06-22   9.868338   9.890044  196.66  20160622.0        2.0  205.950   \n",
       "\n",
       "               ...         low    open  p_change  positive  pre_close  \\\n",
       "2014-08-11     ...      255.00  255.48      4.51       1.0     248.13   \n",
       "2014-08-11     ...      255.00  255.48      4.51       1.0     248.13   \n",
       "2014-10-10     ...      235.20  244.64     -7.82       0.0     257.01   \n",
       "2015-01-14     ...      185.00  185.83     -5.66       0.0     204.25   \n",
       "2015-02-12     ...      193.28  193.57     -4.66       0.0     212.80   \n",
       "2015-07-08     ...      254.31  259.32     -4.82       0.0     267.88   \n",
       "2015-07-21     ...      266.55  270.05     -5.49       0.0     282.26   \n",
       "2015-08-06     ...      236.12  249.54     -8.88       0.0     270.13   \n",
       "2015-08-17     ...      250.51  255.56      4.87       1.0     243.15   \n",
       "2015-11-04     ...      225.20  227.00     11.17       1.0     208.35   \n",
       "2016-01-04     ...      219.00  230.72     -6.92       0.0     240.01   \n",
       "2016-06-22     ...      195.75  199.47    -10.45       0.0     219.61   \n",
       "\n",
       "                volume  \n",
       "2014-08-11   8101276.0  \n",
       "2014-08-11   8101276.0  \n",
       "2014-10-10  12898280.0  \n",
       "2015-01-14  11551860.0  \n",
       "2015-02-12  15649610.0  \n",
       "2015-07-08   6221077.0  \n",
       "2015-07-21   6108686.0  \n",
       "2015-08-06  14623754.0  \n",
       "2015-08-17   7176690.0  \n",
       "2015-11-04  12726366.0  \n",
       "2016-01-04   6827146.0  \n",
       "2016-06-22  23742414.0  \n",
       "\n",
       "[12 rows x 15 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jump_pd = pd.DataFrame()\n",
    "# axis=1即行数据，tsla_df的每一条行数据即为每一个交易日数据\n",
    "tsla_df.apply(judge_jump, axis=1)\n",
    "jump_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3YM50AOgJjcMENIk+3faCUK/73V7TqFse2O9349afXoJVh4Caqdcb9z4qUGxtMCKiOBPH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\n1tah6ezZuPu8ioJ73z1eiOHpHBo6kpf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VJOkVSZL+KElS9S7wNZkQvPyqhEvxlPaOyAGB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\nAMSZsqrSfhUGj7RwBS78+6nMkD7cuEbFr20NedvUYWnKmpqjQxuq25b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4KCfk1u7l7L9aKi98nPz+3Q1sfHl7i4RAAUCgUmkwkrKytKSz/H3/8uAKZNu5uSks8AaG1tZePG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ae43c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from abupy import ABuMarketDrawing\n",
    "# view_indexs传入jump_pd.index，即在k图上使用圆来标示跳空点\n",
    "ABuMarketDrawing.plot_candle_form_klpd(tsla_df, view_indexs=jump_pd.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6 pandas三维面板的使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "disable example env\n"
     ]
    }
   ],
   "source": [
    "# disable_example_env_ipython不再使用沙盒数据，因为沙盒里面没有相关tsla行业的数据啊\n",
    "abupy.env.disable_example_env_ipython()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "abupy.env.g_data_fetch_mode = abupy.env.EMarketDataFetchMode.E_DATA_FETCH_FORCE_NET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "get data...: 100.0%\r"
     ]
    }
   ],
   "source": [
    "from abupy import ABuIndustries\n",
    "r_symbol = 'usTSLA'\n",
    "# 这里获取了和TSLA电动车处于同一行业的股票组成pandas三维面板Panel数据\n",
    "p_date, _ = ABuIndustries.get_industries_panel_from_target(r_symbol, show=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.panel.Panel"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(p_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.core.panel.Panel'>\n",
       "Dimensions: 6 (items) x 502 (major_axis) x 12 (minor_axis)\n",
       "Items axis: usF to usTTM\n",
       "Major_axis axis: 2015-05-06 00:00:00 to 2017-05-04 00:00:00\n",
       "Minor_axis axis: close to key"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>p_change</th>\n",
       "      <th>open</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>date_week</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-05-06</th>\n",
       "      <td>40.08</td>\n",
       "      <td>40.95</td>\n",
       "      <td>39.81</td>\n",
       "      <td>-2.12</td>\n",
       "      <td>40.84</td>\n",
       "      <td>40.95</td>\n",
       "      <td>1460175.0</td>\n",
       "      <td>20150506.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.416097</td>\n",
       "      <td>1.411197</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-07</th>\n",
       "      <td>39.96</td>\n",
       "      <td>40.05</td>\n",
       "      <td>39.63</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>39.65</td>\n",
       "      <td>40.08</td>\n",
       "      <td>1183477.0</td>\n",
       "      <td>20150507.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.416097</td>\n",
       "      <td>1.411197</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-08</th>\n",
       "      <td>42.63</td>\n",
       "      <td>42.90</td>\n",
       "      <td>41.30</td>\n",
       "      <td>6.68</td>\n",
       "      <td>41.61</td>\n",
       "      <td>39.96</td>\n",
       "      <td>2265911.0</td>\n",
       "      <td>20150508.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.416097</td>\n",
       "      <td>1.411197</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-11</th>\n",
       "      <td>42.04</td>\n",
       "      <td>43.38</td>\n",
       "      <td>41.92</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>43.09</td>\n",
       "      <td>42.63</td>\n",
       "      <td>1535718.0</td>\n",
       "      <td>20150511.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.416097</td>\n",
       "      <td>1.411197</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-12</th>\n",
       "      <td>41.41</td>\n",
       "      <td>41.77</td>\n",
       "      <td>41.27</td>\n",
       "      <td>-1.50</td>\n",
       "      <td>41.57</td>\n",
       "      <td>42.04</td>\n",
       "      <td>994259.0</td>\n",
       "      <td>20150512.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.416097</td>\n",
       "      <td>1.411197</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close   high    low  p_change   open  pre_close     volume  \\\n",
       "2015-05-06  40.08  40.95  39.81     -2.12  40.84      40.95  1460175.0   \n",
       "2015-05-07  39.96  40.05  39.63     -0.30  39.65      40.08  1183477.0   \n",
       "2015-05-08  42.63  42.90  41.30      6.68  41.61      39.96  2265911.0   \n",
       "2015-05-11  42.04  43.38  41.92     -1.38  43.09      42.63  1535718.0   \n",
       "2015-05-12  41.41  41.77  41.27     -1.50  41.57      42.04   994259.0   \n",
       "\n",
       "                  date  date_week     atr21     atr14  key  \n",
       "2015-05-06  20150506.0        2.0  1.416097  1.411197  0.0  \n",
       "2015-05-07  20150507.0        3.0  1.416097  1.411197  1.0  \n",
       "2015-05-08  20150508.0        4.0  1.416097  1.411197  2.0  \n",
       "2015-05-11  20150511.0        0.0  1.416097  1.411197  3.0  \n",
       "2015-05-12  20150512.0        1.0  1.416097  1.411197  4.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_date['usTTM'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.core.panel.Panel'>\n",
       "Dimensions: 12 (items) x 502 (major_axis) x 6 (minor_axis)\n",
       "Items axis: close to key\n",
       "Major_axis axis: 2015-05-06 00:00:00 to 2017-05-04 00:00:00\n",
       "Minor_axis axis: usF to usTTM"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_data_it = p_date.swapaxes('items', 'minor')\n",
    "p_data_it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>usF</th>\n",
       "      <th>usGM</th>\n",
       "      <th>usHMC</th>\n",
       "      <th>usTM</th>\n",
       "      <th>usTSLA</th>\n",
       "      <th>usTTM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-28</th>\n",
       "      <td>11.47</td>\n",
       "      <td>34.64</td>\n",
       "      <td>29.10</td>\n",
       "      <td>108.14</td>\n",
       "      <td>314.07</td>\n",
       "      <td>35.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01</th>\n",
       "      <td>11.42</td>\n",
       "      <td>34.20</td>\n",
       "      <td>29.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>322.83</td>\n",
       "      <td>35.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-02</th>\n",
       "      <td>10.92</td>\n",
       "      <td>33.20</td>\n",
       "      <td>28.89</td>\n",
       "      <td>109.24</td>\n",
       "      <td>318.89</td>\n",
       "      <td>34.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-03</th>\n",
       "      <td>11.07</td>\n",
       "      <td>33.48</td>\n",
       "      <td>28.92</td>\n",
       "      <td>108.87</td>\n",
       "      <td>311.02</td>\n",
       "      <td>34.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-04</th>\n",
       "      <td>11.00</td>\n",
       "      <td>33.20</td>\n",
       "      <td>28.97</td>\n",
       "      <td>108.86</td>\n",
       "      <td>295.98</td>\n",
       "      <td>33.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              usF   usGM  usHMC    usTM  usTSLA  usTTM\n",
       "2017-04-28  11.47  34.64  29.10  108.14  314.07  35.67\n",
       "2017-05-01  11.42  34.20  29.08  108.30  322.83  35.17\n",
       "2017-05-02  10.92  33.20  28.89  109.24  318.89  34.81\n",
       "2017-05-03  11.07  33.48  28.92  108.87  311.02  34.70\n",
       "2017-05-04  11.00  33.20  28.97  108.86  295.98  33.97"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_data_it_close = p_data_it['close'].dropna(axis=0)\n",
    "p_data_it_close.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x112d154e0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Di/kUVjzbMDAzGdzNzQAkn3mas5/9NN13/T6RW166zKMTQgghhBDLpbZ0t5gbAcDj76hc\np6guUFxgm7jcTSiKQqRnJ7ZlkIz+CgCXN0Igcvl5Pb4EqnN2P/BOTdN2Ac8ACWAf8KimaUmcrr5P\nnmsnuq5bmqb9HFij63pC07RFG/DKCVQ7ShnVeH2g+sJzAxw7FENVFYIhL919zQwPJjh5dBSAJx4+\nPiVQzeeKlcxsOjXz4sPCMfzvXyHx6COs/8g/4O3uIf7QAwAUY9Fz3FMIIYQQQlzMzLpANQaAx99Z\nt43q8mEZGVS3831eURRa+m4jlzxBMXsWy8ic9+OvtGZKy0XX9Tiwc5qbvjDD9ndN+vlLNZffX3P5\ncwszwqkaP1Atmrg9Kk1hH6qqTCn9nRhz1la9+70vwetzns7uXSd5etdJwOkWbNt23TzUkeFU5XIq\nKYHquSQefQSArK47/x86CICZlrJpIYQQQohLWW1G1SnjnRqolqc+utzVPjNOZvVljJz4Jr6mNef9\n+JJRvXg1/BzVYsHE43WhqgqhZh+JiWzd7RPjWYJN3kqQCrD1qh78Aedny7TJZop19ymX/YJT+ivm\nxkynmCgFrQBWOjXL1kIIIYQQ4mJm2zZmzTqo5c6/taW/ALbl9DVxeZrqrg+2aHRuvJO2tb9x3mOY\n3KxJXDwaP1Atmng8LgDCET/ZdLHSYMk0LFKJHJHWQN19Qs1+7n7vLVyzwyn5HR+pz/yNRJOVyxKo\nzl1hcJDEY4+iBp2DjGRUhRBCCCEuXZaZxbaqCSEjP4rL04zqql+SxradQFV1T125IxC5HLe3+bzH\nIBnVi1fjB6oFEyU1QfyXD9Pc4gSk5fLfxEQW24ZIW2Da+7a2O38M46P1de8jQyk8Xhden4t0avbu\nwAIUr3OmKrn7ScxkkshLbkHx+SRQFUIIIYS4hJl1a6A6JmdTAbCdJNNiLCUjc1QvXisiUCUZZ/S/\nvkso7Pxylzv/luenTs6ollUC1ZqMarFgMj6aoaM7RFPYRyohGdXZ2IaBXShULgNEXrYTV1MTppT+\nCiGEEEJcssyi813Q5a6uoDF1fmqVoix8exzJqF68Gj5QtW1wWQZmMoE/Ow5UM6oT4+cKVJtwe1QO\n7DnL07tOYpoWozHnD6qjO0Qo7KOQNyiWSonFVEYyWfdzYLOGt7cPV1MTlmRUhRBCCCEuWabhfBd0\n12RRZw1UFyGoXIwsrWgMDd/1F8BlO7Xv6qnDQM80gWq13t3K5VD9fgA8XhevfuN2HvrxIXbvOsnx\nwzF6VkUA6OwOU8w7AWoqka9kX0U9M5mo+znyUqertdoUwurvxzYMFPeK+DUSQghxETMsC1VRUGu6\n/AshFlc5o+rxd5JPnSxdnlr66w9vJJc8jjfYs+BjkIzqwtI07QHABWwBosAY8HPg74GPA1cCPiAN\nvEvX9eOapj0M3Kvr+qFp9vfnwJ8CG3Rdz02+fTYNn1EF8JfXVjr4DACD/XGiZxNTMqqJJ5/g6J+8\nk+Qzuyv3XbOhjd/5/RvYenUvo9E0+59z2mZ3dIdo73LKFHb9/AimYS3V01lRzIQz96DlFbfT/Xt3\nE77xJgBcTaWGStnzX/dKCCGEuFCWbfODU1Hue/YYPzszutzDEeKSYpUyqrVZVPc0GdWODW+ie/M7\n8IfWLfgYFqOc+FKm6/ptuq7fCtwP/Lmu67fquv4R4NVAn67rd+i6/jLg88An5rDLtwHfAN4y37Gs\niHe2NXsWFAXXRAw6ITqY5LtfeZZA0EtTyIvH66IwPMTwV74Etk326FHC111fub/P7+bW12hctrWT\nh3/srAXa0h6ktSPIwKlxTh4d5cH/Psjtv7ENRVHI54p4vG5UVc7Kmgmn9Ne3eg2Rl1XXCHY1OUG+\nlUpB+Pw7tV2KsnmD/miK6HiWaDzDqo4QN27rXu5hCSHEinQ6leOJqHNS9UA8xavXTNPIRYjz9FD/\nLtY1r2ZjZP1yD6UhVTOqzt+d6m7C5Z46JU91+fA1rV6UMSiKQlPb1Yuy78XwzM8++DHgTQu8229d\n98qPfXC2DTRNuwvYouv6hzRN8wOHgI8CbwcsYLeu6++ZZRcx4MWapv0O8ADwfeDH53jMW4FjwOeA\nrwFfmsuTKWv4jKoCtGSHORXoQQHcOA19bBsy6QJNagGrWODs5z+LnXeyycXhoWn3tXp9G2+990Z+\n554bcLlUVFXljtdvo2d1hKMHY+z6+VGSEzm+/OnHeXrXyaV5gg3OKJX+uprrg1G1SZaoOR+GafEX\n//IE//Dvz/JvPz7Ij351ii/9ZEqVhBBCiDnqT1UryUZzRfKmVEiJhTGRT/DtIz/gH5/5zHIPZVqJ\nQpLvH/sJmeLyVbeZRrX0t/b/pda+7vXL8rgXgbuBd+u6vgM4qGnajElMXdd3A/cAbwD2A08DO86x\n/z8AvqDrug7kNU27cT6Da/iManNIwWMVONm0ivb8ONrAQzy19g6aSv2P3P2HOXXfTygOD9N8y0tJ\nPfsshejwjPtTVRW1Jjx3e1y89re381//vocXnh1gYjyDaVgc2neW61+6HmWB5rpkMwXyOYOWtpU1\nF7Zc+js5UHVJoHpejg8mmEgV2Lw6wo7tPfzi6TMMjKSxbXvBfteEEOJS0p92AtXtrSFeGE8xmMmz\nITx9k0Uh5sOwGrvZ5lNDz/KzUw9RNIv89ubfWJYxmEYa1RXA5Wmifd1vLlugupKUMp+zZj+XQPlL\n593ABzRN2wA8XnP9FJqmXQXouq7fqWmaAtwB/KemadNOPNY0rRV4LdCladqfABHg3cCTcx3kkmZU\nNU3zaJr2VU3THtU07SlN0875V9UadBopbd3cQ/uNN9CTHSCfqgaigWKC4vAw3t4+uu58G97uboqx\nGLY594OLz+/hlW/YBkD/CaezcDpZYHggMdvd5uXhn+h858vPYK6wM71GwnkN3OHpA1Xp/Ds/B085\nv1+vvGEtO1+0ipawsyC2YdrLOSwhhFix+tM5Qm4XV7Q6U1IG0vPq1SHEjIpWYbmHMKt4zkkm7Bp8\ngol88hxbLw6rmMblcf72mtquXJRmSWLB5IDe0uVrS//fg9MEaSdwDXDzLPe/HfhrTdNUXddtnKxq\nunR5Om8D/lXX9Vfquv5q4EbglZqmzflsxlKX/r4NGNV1/aU4E3I/fa47NLucQCkcCdH5Eue1uyx5\nrBLvB4sJvL199P7RO1F9PtxdXWCaFMfm11Chtb2JSFv9Gdhjemxe+5jNWCxNIW+STq6sdVuNsTFn\nfnBLS931ammOqqylOj8HTo6hKLBlrfN6elzOn2BRmnkJIcS8JQoGEwWDNSE/q5ucE39nJFAVCyRv\nNnagOp6PA1C0DB7o/+WSP75tmVhmFtXdtOSPLc7L/cB6TdN2AW8GEsA+4FFN0x7E6fA7W7bzn4EU\nsKe0j/8Afrfm9m9rmvZ06d/Hccp+v1q+Udf1DPAdnOB4Tpa69PdbwLdLlxUoTTidRSqRphOItIUJ\nXL4ZNdzM5vRpHnfdjGJAx2WrWf8nfwjA3mOjPHcsxw5wsqydXfMa3LpN7ewdO0P3qmaGBxKMDC9M\nEGbbNqlEvvR88jS3rJySpGIshrulBdVT3/rbFSoFqikJVOcqXzQ5PphgfU+YoN95Pd1uJ1A1Vlim\nXQghGkG57HdNk59WnwcViBfO+dVCiDkpmMXK5UacojOen8CtuAh5Qzx65nHuWHsrYW9oyR6/vIaq\nSwLVFUHX9Tiwc5qbvjDD9ndN+tkA3jfDtrfOcQx/PJftypY0UNV1PQWgaVoYJ2D9y3PdJ592lqBZ\nt76Lru4Io9u2YD35FEZ2jFYLVl3VQ2dnmPFkji/+5CCrXM4fqC8Tp7MzPK/xXXvjOvbuPsO2q/rI\nZYok47l572M6iYksluVkxRWUBdnnYuvsDGMZBofj4zRv0aaMOWus4gzgzqZWxPNpBCcGJzAtmy3r\n2yuvWbjJWaQ6HAnQ2Tr9/GV5fRuDvA/LT96DxtBI78PoqFN1tX1VK90dzQQ9Loo01hgX06XyPJfL\nGcNVuRxu9RLw+Kfdbrneh0QhQXuwlddpt/Fvz36TJ0af5K1XvWHJHj89EWcQCDW3LevvovwdXLyW\nvJmSpmlrgO8Bn9F1/evn2j6TcM7W5IoQiyWhw1nGY93o82xNncK+4x6i0QSf/PZeJlIFgr5WAGL7\nD+O+4aXzGlsg5OHN73gxLe1Bjhwc5szJcQYHxvF4L+xlOntmonJ58Eyc3rWRC9rfYuvsDBOLJSlE\no2BZEGlzXvsaluUEWKmzw1NuE9PTj48AEPa7K6+ZaThzqYejSRRj6rzq8nshlpe8D8tP3oPG0Gjv\ngx5LoAAhwyIWS+JTVZK5YkONcbE02nvRqCzb4v/t+wpXtm/lJavm1XCU2Fi1V8mps1HaA61Ttlmu\n98G0TOK5BJta1nNV+Coi3h/zk8MPsaPjJkKepclwZieiAOSLnmX7Xax9/SVgvfgsdTOlbuBnwP+n\n6/q/zeU+Zt6ZH6D4nMDI27fK2VfqNACenh4efHaAvcdGuWJ9K9tuvIK84iFx8BCf+s5enjgw/VI1\nM2nvCuFyqbSU5qtOjGfndL9iweSxB44yPjq1uVByojpf5szgwjVoWmzFEWeOrrtj6pp0qteLKxSm\nOD621MNasaJx53epq7Va+u1xOWdrDZmjKoQQ82LZNgPpHJ1+L/7SsTTgVsmaFrYtDeqEI56fYN/I\nAXYPPzfv+xZq5qhmjLl9H1wq8XwCG5tWXwsel4fb191K3izwq4GnlmwMldJfz9KVG4tLy1I3U/oL\noBX4K03THi79m3XCpsd25geoXqdJgq8UqKrY2IrKmDfCfz50lFDAwztet42rLu9kwN+BMhbj0KEz\nPLF/5qVqZhMplWHGx+Z2YHr28VPs3X2Gh36sT7ktlagGqgeOjnBqaHHOOpkL/OFcDlQ9HdM353K3\ntWGMjckXgjmKjk8TqJbmqBZljqoQQsxLNFugYNmsCVXLMYNuF6ZtU7Tkc0k4kgWnl8ZYbnze9y3U\ndP1dyrVKJ/IJfnj8pxyLn5xxm3jeqdZr8TlVetd0XgnAmdTgoo+vzCw6r63MURWLZannqL4XeO98\n7tPd5IIEKD4nUPX0VNtej/Rexp7D4xQNi3e8diutYR+GafFkoJuN2bOszkaZSJ3fek7lDsATY3M7\nMJ0+5mQW89nilNtqM6peYNe+s6zrWdjyhGLB5GuffYK1m9q47de2Lsg+jRGnVNXTOUOg2tpK/vQp\nrEymslyNmFk5UO2saabldjuNGaTrrxBCzE9tI6Uyf6mTetY08bqW+ly8aETlQHU8P4FlW6jK3H8v\napsppY2lCVSThRR/8+THyRo5jk+c4r3X/OG025U7/rb6nVUEIr5m3IqLkezSVbpJRlUstiWfozpf\nl3UFSJ8FtVT6W9t99oW2LZilILC83Edr2MdAabHhVbkYz6XPbzmYlrZSRnWG0t9EPMve3WfI5wwM\nw2Ik6hwI8/mp3QbLgWpBAa8NT71wlhtWt3DZls4F6yA3MZ4lly1y+IVhtl7Vy2g0xaljo4RbAux8\n1eZ572/ku99m7Mc/AsAzTekvOBlVcJawkUD13KLjWVpCXnyeanOGyvI0klEVQoh56U+VAtVJGVWA\nrGER8S7LsESDSZQCVcu2mMgnKoHdXNSW/maLS1P6+8zw82QN53c7VZh5ZYXxnBOoljOqqqLSHmhj\nJDe/5RkvhFXKqKoSqIpF0vCBql1wDhLl0l+Arrf9Hr/66VPsMdroS+RxqUqle6rbpWK0dMAgNBtp\nEukClmWjqvUBYXrfXiYe20XP798zZekVgHDEh9ujEpumTNc0LX763f2V4BRAUcC2IZsukssW8Qec\nfZqGRWwoSbDJSzydpwWFdXmLX3z/AH7/VazZ0HbhLxLUrc/6/a/vqbllnJt2bsDnn/ocZ2JbFvGH\nHgDA29uHu2Vq8wAAT6sz9uL4GL41a+Y/6EtI0bAYS+S4fE39B6Sn9KVKMqpCCDE//ekcHlWhK1CN\nSAOluaqZaZrTiUtTbbA3no/PL1C1lj6junv4ORQUPC4P6eLUvidlsaxT9dYZaK9c1x5oYzgTI2tk\nCbgXfynE6vI0069aIFYmTdMeAFzAFpy1VceAnwPbgFXAeqAADOKsw/od4CHgTl3Xv1Gzn73As5OX\nuZmPhg9/HT3pAAAgAElEQVRUrXweXC4Ud3WoLbe+gtMTXeQPRumPJmkN+1BrMpOB9hZsIGRksW1I\nZAq0hHx1+40/+AvS+/aSv+OVBDZdNuVxVVWlZ1WEMyfHyWYKBILVD8L9zw0yEk2xeXs3N7x0Ay63\nisfj4unHTrLnyX7GR9L0lgKSY3qMXNZg84t60fcM0IJCAGes/f3xBQtUUzWB6tpNbay/rJ3YUIqD\nz58lNpRi9frpg83pZAcGsbJZwjfuoOcP/nDGrK+7FKgO/vMn6Lzzf9B62x0X9iQuYk8cGMIGetrq\nD+Zul/PaSjMlIYSYu7xpEc0WWBcO4Kr9/C/N+89JlcqyOj5xki/v/wb3Xn03vU3dyzqWZLEaqI5l\nx9kYWT/n++ZrmyktQUY1mhnhZOI0W9s2kzPynEr2z1iuPJSOOkseBqtVbx1+J2gdyY6xJrxq0cdr\nFtOo7iCK4jr3xmLF0HX9NgBN074EfEPX9ftrb9c07T5gSNf1z5V+vhU4BLwF+EbpuiuBCy63bPxA\ntVBA9U6t3+lrd567Ydq0heuD0LaWJjIuP02mc1CZSE0NVHOnTwFOw6ByoGrZNhSLnP38Z2h5+Svo\nW+MEqmf7J9ioVedpnjrqlFXsePkmgk3VsbW2O0HI2EimEqgeeM6Z1N62JsLongFq846nT8W5eX4v\nx4zKGdXfuPNqVq1zgtJjh2KlQDU5r0A1efgwAIFNm2YtTS6X/gKM/eS/JVCdwZ4jI3ztZ4cJ+Ny8\n5qa1dbdJMyUhhJi/M+kcNvXzUwECbsmoNoJj8ZOM5MZ4auhZXr/pNcs6lkShWhk3VprXOVfFuq6/\ni59RfbrUmfj67mt4PvYClm2RMbLTLjczlInSEWjDo1a/yncGnO9lSxaoGincHlkSZr7u+fGzHwPe\ntMC7/da/vPbaD862gaZpdwFbdF3/kKZpfpzg8qPA2wEL2K3r+nvO8/Gfdx5Ci+i6PgG8Dfh3YO3s\nd5tdw3casPN5nn/RzRyM19fp93VU/2jbmus/qNqa/aRrAtXxVP08VSMex5xwuqUVY05n22MDE9z7\n8Yd54Vf7SD+/h8STT9Bbmvc6eLp6YLNtm+jZBJG2QF2QCtDW6YxpqLRu6lgszdkzE6xe30oOKAJN\nHdWMWmJ04Q565c7CoZrXoqvXOXhMV748674OHwHAv3HTrNv5+lah+JzHcwWk7GM6Tx0c5lPf2YsC\nvPP1V9DdWv86VQJVyagKIcScnak0Uqo/CR2sNFOSY+pyKmciD45OXQlhqaUK1fLZsdz8AtXajGp6\nkTOqtm2ze/g5PKqHqzuvIOx15n0mp5mnmiqkSRcz9DR11V3fHihnVBd/nqptGdhmDtUt81NXuLuB\nd+u6vgM4qGnahSQxvwO8UdM0BbgB+NWFDq7hM6p50+SZq27ihePDvP9KPyGPM+TemkC1tbn+g6q9\n2U/KFaCrEMdtGUxMClQTR49VLpeXYHng2TMYps3RIwNsB4zxcfp6m3G5VQb7qwe2ifEshbzJ+sua\np4y1oztMa3uQIweGefEt6zmwx8mmXnFNHwfHnAOldv1qfEWLn/7iCOGcQSFv4PVd+NtQLv1tCleD\n51CzD3/ATfTs/ALVpH4ExePBt3r2eaeucJhN/+dTnP7b/4UxtnST91cK/fQ4X/jRAXxeFx94yzVs\n7Jv6O+N2SaAqhBDzVW2kVD8Pz19ppmRi2TbfOTFMNFvgndvW1E0REourvKxLf2qQiXySiG/5sm7J\nYgq34sKwTUZzUzvi2raNZVu41Knlq3VzVGeZL7oQTifPEM2McF3X1fjd/ppANTmlfHooEwWgJ1h/\nfUcpo1qev7qYqh1/pZnmfJUyn7NmP5dA+YB4N/ABTdM2AI/XXH8+vg58FjgOPHphw3M0fEa1vBZa\n3rR4aLC6BlZ3awCXquAKuhlvcmFY1S/67c1+0qWJ3SEzy0SqULfPZx5+trr/WIxs3uBZ3QlYx4ad\nP24jPo7LrdLd18xoNE0+5xysooMJADp7px50VVXhupesw7bhqUdPoL8wRDDkZd1l7YyVAsm+3mZe\n9OLVWF4XCtQ1ZLoQ6WQef9CD21090CqKQldfM8mJHIn43M4EWvk86VOn8K1dVzcveCaqx4O7pQUr\nm3XmEwsABmIpPvWdfdg2vPuNV04bpEI1o2rI2X8hhJgT27bpT+do9riJeOs/p4KlY2rWsPjFwCjP\njSYZyORJSynwkqpd1uXQ2OFlHIlT+tvmb6Xd38rx+CkMq351hgf6H+HPH72P0ezUdVbLXX/9Lv+0\nmc2FtLtc9ttzDQBhr/M9MzHN4w6lhwHoDtYvH9gd7MStuOhPLv5aqrKG6oqUA3pLl68t/X8PcK+u\n6zuBa+D8ZyXqun4cZ17qe4CvXcA4Kxo+UC3UBKBPxuKM5pyDhtul0tkWoOXKDk5i8PRIorJdT3uQ\nlMspSW0yssTT9YFqrr8fgKLqphCNsvtQlIJhoShgJp0/PGPcyaL2rXHafg/2O+W85exk3DCx7akL\nil+2tYtIa4CjB6IU8iZbr+rF5VIZTzhBXFsp+xtqdc4Cnzk9vzKU6di2TSqZJzRpri7Axs3OQezo\nweic9pU7dRIsi8A5yn5rlbsCG+PzX0z7YjSezPOJbz1PJm/wjtduZdv6mRtmSemvEELMz0TBIFk0\nWROa+plX7vp7MJ7i4bPVz6REYerScWLx1C7rcmBs+cp/LdsiVUgT8oa4smMbOTPHkfjxyu22bfPY\nwJPkzDzPxfZOuX/BLOJSXLT5W4jnE1NuX8hxPjP8PE3uIFvbnCUFZyv9jWacpEr3pNJft+pmVbiP\ngdRZijUnCxZlzLKG6kp0P7Be07RdwJuBBE7X3kc1TXsQp8Pvkxf4GN8E1ui6viBnqBq+9NdwOcuq\ntHjdxAsGPz0zylsvc04GNK9rJhFybn9sKM4NnRFURaGnLciOmzbD/fsJmVmOnpkgmzcI+NzkCyat\nxQR5xU3U28bq8RgHH3uOW0cOsM2TYCTjfJjZ+RxWLkvf2hZ47BRnT8fZcHkH8TFnXulXHj6Gt9nH\nTdt66sarKArbr13FYw8cRVFg69XOWGPxLAGfi2CpzLe7t5nR4TT9/RPccIGvUSFvYBQtmqYLVLUO\nHvnZYY4ciHLtjnXn3FfuuFMWfa75qbXcrc5cXiM+jren5xxbX/y+v+s4Y4k8v7VzIzu2z/56eKT0\nVwgh5qW/Mj/VP+W2ctffRNHE71LZ3hri6ZEEEwWDVYuc+EkVDSwbmr0N/9Vq0ZXndvpcXg6OHaZg\nFvjygW+yo/fFbO/YumTjSBcz2Ng0e0Nc3XkFD595jEfPPI5X9dIV7CBRSBItlcnujR3g9rU76+5f\nsAp4XR4ivmYG00PkjDx+99TvWhdKHz9KopDkllU34S41R2ouZVSnC1QnCk7Q3OKbWq21vnkNpxL9\nnEmdZUPkgvrYzKqcUZU5qiuHrutxYOc0N31hhu3vmuH6+yb9/DDwcOnyp4BPlS7fjxMcn7eGz6ga\nbicQ3d4aYk2TnxfGU5xOOWWsTV1Oee+2liZG80UOxavzB3rXOQHi1nYXZ2Ip/u6rzzASz9IfS9JS\nTJH0NzPuCaNg8/Jnv8VN8f00x/rZmD1bfezxcbr7mlFdSmWeajpVwMRpjXX/E6enHfOWq3oINnnZ\ntKWLcMSPZdkMj2fpbg1WuuhuWNeCic14zPlDz+eMaTO0czEy7OyjOTL1Q9vn97B2QxtjsTQT4+du\n3nRegWqklFGNS0Y1mzd48kCU9mYfr7nx3CcG3FL6K4QQ8xLNOkFQb3BqwOBRVbyqggLcuamHTc3O\n94SJRcyons3kyRgmnz3Qzz88f4LPH+zn8eE4qeKlm8UtZ1S3t28lXczwvaP/zZ7YPj6794tLOo5M\n0fne0+QJsimygZCniedH9vNPz36GD+36a/5h9ycBJxN5fOLklKCwYBbwql5afE51XaKwOFnV3UPV\nbr9l4dLcz2Rhap+R8jjD02Qz14Wd/iKnEv0LPs5aMkdVLIUVEKg6Z5a8LpXXrHHWirr/zCi2bRM3\nTdp9Hu5Y7XQ5e3SoGii5Is5B5aa1AW67bjUDI2n+5itP8+RTx/DaBt7OLo41rSbl8nMgtJ7Uy18/\n9bHjcdweF129zYwMp8jnDDKpPHZp7cvT0RTHBiem3M/rc/PWe2/kFb+2BYCxRA7DtOrW0FzXEyYD\nFNNFYkNJvvjJXfzom3src2Hn4/B+Z67Chs0d096+dpPz+vSfmD6QHB7LUCg683dyJ47jaW2pW3rm\nXNyt5dLfCy9jXul2H4qSL5q89Ko+VPXc89EloyqEEPOTKAWAEa9n2ttfv66L/3FZL5dHmirZzcQi\nBY2posGn9p/m7/ccZ7xg4FNVTqdy/PB0jE/v76dwiZ6ELDdTurpzOwCPDDwOgHJBfVrOZxzOdyqv\ny4tLdfFn1/0xb9V+i9vX7uTqjivoCXaxrnkNt615GTY2xyZO1t/frGZUgQUr/00WUhwvPVbBLLAn\nto82fysbI9UT3NPNUf3+sZ/wD7s/yUQ+QcDtx+Oa+jewrrkUqCYXOVCtzFGVjKpYPA1fn2J4nC62\nXlVhfTjA2pCfU8ks0VyBrGlxWXOQ7oAPLRJEn8jQn8qxJuTHXQpU00/v5vV3Xk7PHZv5+i8Oc2jP\nEa4HWtb0MZDYwKdD6/B5XXzijTs4/csfQs2c2PKcy761EYbOTDBwapxc1qDgqh5oH9kzyKa+yJRx\nezzVpkZDpUxmd02g2tESIK8qhC04+PxZbBvOnBznqUdO8tJXXj7n16dYNDl2KEao2eeUKU9jzQYn\nkOw/Mcb2a+vX1YrGs3z4/z3B1nWt/PqmCNFiC1u2dc66fupk1TmqY+TP9JM5cAB3WyvhF19oUfPK\nc6SUeb9+a9c5tnTIOqpCCDE/5ezo5EZKZdd0VMshy9skCgbD2TzJosllzQu3nFqydJLXLBVE3dQd\nYUdXCz/uj7F3LMWRRIYrWi+9L/IFs4BH9bC1bTOqomLZzmfc5O61iz+OUqCqOt8lu4OdUxoQAewf\nPQSnYCB1lheVgmtwAt2QN0TE6/xOTSxQoPqvL3yNI/HjfOC6dzGenyBvFti5+iWoSjV/FHD78age\n4nknIZIspHiw/1EMy0BBoTPYPu2+u4IdqIpKLLO4qzFYklEVS6DhM6pmKaPqUZ2h9gV92MCeUacU\nYlVpDbVbepxgqZxV9XR2EXrx9RRHYgz+8/9h+4EHeN8bttKF84fVsnYV67qdD4/rtS78AS+e9vo/\n+nIpa98aJwAsNyTKmhab+prpiPh56mCUbH72M7XDY06pcndbtY2+qigESqW6xw5VGx0NnJ5f+ezx\nQzGKBZPNV3RPCS5HT/+Q6LGv09wSINIaYOBUHHNSQDReWn/18KlxHnrwNPt7dvLT9FZeeHYAozi3\nLonlOarxhx7g1H1/Rew//4Oz//J5zMzitnJvROXuzh3TlGFPp5xRNSSjKoQQc5IoGPhdKj7Xub/C\nNJdOGj83muSTL5zm3/SByvShhTC5m3CX30uz180t3c53kgPji9sptlHlzSI+l5egJ8D65upSdyHv\n0gbt5cyud5rMY61VIWe62GDqbN31+VLpbzmjOrFApb/lhk5PDT3L/pFDAFzTeWXdNoqi0BXsIJod\nwbZtfjX4VKVjsY09bdkvgKqotPoijOUWdzqWaZTnqEqgKhZPwweq5Tmq3lIZZXfAOSv23IgTqPYF\nnYBgYzhAX9DH/vEUY/kiiqrSd++7WHff3+BdtZqJXz5E+Kv/zK93ONnNQE83m9e2ogAvu7oPAE9H\nfRasHKj2rIqgqgqnjjpnpwpAS8jHS6/qJV80efLgcN39ipM6Ag+VGjDVlv4CdPU4ZR25rHPgaWkP\nMj6SIZede/nv/uecFuTlpk1ltmWQGdtHLnkCcLKqxYLJ8GD9QTZfdAKkcggdLEyQNVQe/dkRvve1\n5+Y0Ble4Ge+q1bhCYcI37qDpmmvBNMkcODDn53GxGEvkCAc9eNxT12ObjmRUhRBifiYKxpwbFrlV\ntbJkTVl5DdZalm3zg1NRnhuZXyCSnnRCt9PvfEdZ1eQj4nFzMJ7GsM6v/8RK5pTMOq/Ftjatcv1i\ndaLNGXlOJqb2DalmVGcPVCPeZpo8QQZqAlXTMrFsC5/LW2laVM5uXqjLWzYCsG/kIAfHDhPyNLE6\n3Ddlu65gJwWzwFguzqMDT9TdVi4Nnk6rv4VEITllKR5wXquDY4cv+LmYxTSqO4iiNHwoIVawhv/t\nMtzOga6cUe0OOBnURNFAAfpKGVVFUbi5uwUb2DdWnXjuW7WatX/5P2m5/ZUUhs5SeP4ZZ39dXbz6\nhrX87T03ctlqp3TX1eL8b5Q6ruUHnQOWx+uisydcyXoVsImEvNxyVR+K4pT/lmXzBn/26cf4zH+9\nQNEwOXhyjEOlLGl3a32gum6t01Cp/BibNKccZXhgbh+UI8NJhgcTrN3YRnNL/aLn+cwgtm2AbWLb\nFqs3OHNOz0yap5rJOwfxcv5vXfwF3v3BW2hpDzIaTc2pwZOiqqy772/Y+E+fpPeeP6L9tb8GQHrf\n1FbvFzPbthlP5mkLzy2bCtVmSjJHVQghzq1gWmRNi4hn7jOXMqXj6/qQc2wezExd8/vAeIonohN8\n68TwlNtmM7lhUmfpZLqiKGxvC5EzrbpGj5eK2kD1qs4rKiWtRWtxAtV/2fcVPvb0pzkxUR+sFktN\nnTylscxEURRWNfUykh0jZzi/H/nKfatzVBei9Ne2DLYqGV4X9JHMx5koJNjSdnld2W9Zd8DpPfKL\n079kPB+nJ1hNqDTPkp1u87diY08bjH736I/49J4v8JeP/R2DqaHzfh6mkZL5qWLRNX6g6illVF31\nGVWAy5qDBGsyV2tLH0LD2fp1U1WPl663vJVV7/szXM3NqMEgnrZ2PG6V3vZqyYLqLwUYqkrM20L2\n2BFsw/kQ6l1TnYdaBCJNXlrDPq7e1MHJoSSnh53g+MTZBOmcwTN6jHd94hE+9o09DMTSrO4MEfDV\nf7Cu7QlTLkCKtAboXRNBVU2efETn/u++wM9/cIAHf3SQA3umX7i5nE294pqpZ+HyqVOVy7ZVZNXa\nFlRVof/EWN122Zzz/AKleLSl2UOks4XmFj+2zZzLfxVFqZQe+9atxxVuJr3v+fPuZLwSpXMGBcOq\nrJU7F9JMSQgh5q7aSGnugaqnVJG1s7cNj6pwthSonkhm+eqRQfpTOR6qWXPVmsfnVm3pb8TjritH\nvq40V/aZkYXJwq0kBauArzQvdFWol7+5+cOEPE2VUtyFdmj8CAD9yTOTxjG3jCrAqnAvNjaD6aHS\nfatL7IQ9IRSUBQlUxwd+xlZSbPd5uNrnjKu8dupkXaX5tLsGnWzq6ze9pnJbeLZA1edMyRrL1Te5\nzBpZdg89Czjlw9FM7Lyeg20Z2GZe5qeKRdfwzZSssHOgL2dUAzWB6bUd9etHtfk8uBWl0rp+sqbt\nV7H+I/8bK5dDcU0tzVR9TqDqMgqcjmygc0Ind+okgU2X0be2hT1POh3Urh/bS6u6GnDKhvccHeGX\nzw/yu6/UODVczeZ2RAJs39jGlRvb0dZMbXS0qqOJDBDCKfvt6vVz+61P4fEUKRTcZHN+slkfx/a2\n0971Wrr7qs+3kDc4vH+YULOv0tW31uRA1evz0d3XzNkzE2QzBQJB5wMknS2yHoVyv+CW5lKX5VJQ\nnc+beOa5Jpyiqvg3bSK95zmsVApXeObylIvJWGm+7/lkVGV5GiGEOLdyI6X5rFX6B9pqTiQzXB4J\n0hPwMZDJ8b2Tw+yOOUFHqmhWgldw5sC2+M4d2JTvW1Y+WV7WE/SxusnH4YkMyaJBeB5Z4JXMsi2K\nllE3L7TFF8Hn8lE0F3fJnnIWtKxS+nuOjCrAhua1PATsie5jY2QdmaKTSvC5fLhUF23+VobSUWzb\nnlfDyclyyZOVy7eHW2jp3Mw1XVdNu21X0Pl2ZtkWW1ovrwtoZw1U/c4c6fFJgeruoecoWEW6g10M\nZ6KkjXMvWzid6vxUyaiKxdXwGVUrVA5UqweFa9rD+F0q21rrz+SoikJXwEs0W5jxjKgrEMBTWk5l\nstbb78DT2UX2jXfT73c602V1Z5J77+oI5ePS1sRROr75aaxCgSs3tdES8vLE/mHyRZNTQ06g+r/v\n3cHf/eFNvPX2zVy5sR2vZ2pg7PW48Iacg2ekNYCq5PB4iqjuJgKhCJFIjp7uUbZtPcbjDx6ru+/h\n/cMYRYttV/dOWQbFti3y6Wpbcrt0RrG8fM0jPz1SyXRODKfoxMmGqpaBx+P8SpQD1cI5GkXNxN3q\nlBpfSmurjiWcLzqt58ioGhNxCjHnw05VFFyqglkoYEzI8j5CCDGbc3X8nc6akJ+X9bahKgp9QR+W\nDbtjCboCXjyqQn/aOclYnssay80965cqZVT/dPs63rhhakfby5qD2EAsW2AsX1/2atk2Pz4d4/DE\nxVUaXF5DdXJw6HF5Fi2jWpY368u659pMCeCqzu2EvSF+dfYpckaeWHYEgM6AkwzYEFlL2sgwfJ5Z\nSADbMjHyo0Qtlb0FC8wsr1v1YnwzBNJdNR2Kd66+GY/LQ3Npbuq55qgCdQ2Vckae+08+iEd1c/va\nlwFUgvH5MovS8VcsjcYPVJucPwKvWh3qb23o5sMv2lDJstbqDngxbJuxfJGD4yn2jSXJzzFb5W5p\nZcPff5TO66/jdMD5wJl49JcY8Then5vOnjCKbeIzMqipBIWhs7hUlVuu6iObN3jqwDCnhpI0+d1z\n7vratjrCEDbdG1qxTefDsql1O6uueBdrrv4wvqa1eNwmZ8/EMUofiLZts/+5QVRVmdJECaCQOVsJ\nTgGs0uXt162iZ3WE43qMvbud8pjMWPUg5TMzlUyzz+f8X8idX6BaPhlQHL90AtXxZDmjOnOgalsW\np+77K05++M859r53c+YTH+fq5FFu2fsDjr//fRjJxVlMXAghLgbnE6jW2t4WIuR2cVtfG+/etpa+\nYPV4fU27c2I8No/1zNNFE5cCHX7PtF2Iy+P85dlxPr73JM/WNGs6GE+zazjOlw5PP71npcrPkMX0\nqp5Fa6ZULu2dMaM6h9Jfj+rmZat2kDVyPDn0TCUgLQeLmyIbACrrn54PIz8G2MQthVzpK7htTp0z\nXdbkCdLub6Uz0M72jq1ANVt6rjmqACPZMQZSZ3mofxefef7fmCgkuH3tTnqanLmu6eKFZVRljqpY\nbA0fqCp9ToltbUZVVZRpg1SArtIc1mdiCb569Cz/cWyI78yzOUJ7xE/GHeDohhsoxmKc+cePYkxM\n8Ipf28IV0UdQcQJfK+2cUbr1RX24VIX/fOgow+NZ1naH51wWsrY3TD82z50Yo1h0Ah3FVW0QVb7s\ndpkYpQ69QwMJxmJpNmzuIBiaGhTlU04zAZfHOdtWDlpdLpVXvmEbwSYvjz90jMHTcYxEHgubcQyu\nGH4UxT259Pd8M6qltVUvpYxqaWmatuaZT1JYuRxmMokrHMYVbCKz/wVuG3qc7oTzRcWMX3pzmYQQ\nYq4GM87nZLm77nxtag7yF9ds5LZV7bhVhVVNzvFaVeDKNudL9/wyqgZNbveMn/kRrxMgHUs4AcF/\nn46RLM2zfSpaPd5fTJ2ByxnV8hzVMo/qoWAVF6V3hc/tfBeaOaM6t9+Xl67agVtx8XD/LobTTqBa\nXnd1U8t6AI7FT866j3h+gv/5q3/gmeE9U24r5px9jlo2ZukruGXNHKgCvO/ae/nTa99ZabZUzvBG\nvJEZ71POqD4x9DR/99Qn+PaRH3Bs4gQbmtdxx7qXE3Q7zT0z51H6axSTJKNPAeCaYYkcIRZKwweq\nVsj5I/DOYb00qHYF/uVQNUAazs5+EJjM53Hhdavs7nsxrXe8isLZQc7808cIuwp0J6tzP820c0ap\nrdnPTVd0ky5lH7dvbJvzY125sR23S+EHj53kv37plBmrrmrwqZYO9C63SbHgZFT3PzcATN9ECarz\nU/3hTQB12dWmkI873rANgJ9+bz8UTBKAr9VFJBfDVudW+ntyKDHrvEp3SylQvQgzqv3RFKlplhAa\nLi1DNFtG1co62wS3X8mGv/8o4etvwGVXX0eruLhlUUIIsVLZts3pVI5mj4uW88yoTraqlFHtCfjo\nKX1/iM3Q52LyWJ4bSTCeNwhNM7WnrJxRLR/ls6bFj07HGMsVOZKoBgm1c2QPxdM8cra+8eFKMlNw\nWC6/nW7JlAvlL31vKnfsLStncD1zyKiCM+/zxT3XEM2O8GxsLwoKHQHnO11vUzd+l/+cGdWnh/cw\nmhvj+MSpKbdVAlXTwlSc3xtrUnBt2zapkWco5pzS4zZ/a6XrMMDrNrySu7fdSXtg+mls4DSAetW6\nV7C59TJu7LmOt219M3+948N84MXvwufy0uRxAtX0PEp/8+kBRk/9gGH9X8mnTuALrScQ2TLn+wtx\nPhp+Zn+xdJbRo84tQ3lZc4DVTT7OpPO0+5xSnPKcVXWOWU5FUWhu8pLIFOm46y3YlkX8gZ9z+u/+\ntm47M12dV/JrN6/n2ECC67d08aob1s7x2cHqzhAfuecmvvHAEUbi+2ENqK5qRq42o1osmGQzBY4d\nitHSFqBv7dQGTbZtk0ufxuVtweN3zrrZk9rB961pYccrNvGrB45hA+MKbA05vwoGkwPVqV1/n9Gj\n/N/vvcBdr9lSWYN2skpG9SILVDM5g//1xd1Yts3rdqwjFs8SHc/S0RJg34kxOlv8tM9S9m1lnC8m\nroDzIeFu76i/PbtwC9ELIcTFZDxvkCyabG8NXVAzm1rrwgFUxVmL3etS6fR7OJPOYVgW7hkqtwAO\nTaQrS9kE3DNvVxtQB1wqnX4v+8ZSlSZMG8MBjiez9KdzrAn5SRYNvnl8iLxpcUVriPbzzBwvp0pG\ndUqg6vxcsIp45jBndD585UB1ckZ1Hs2Uyl6++haeOPs0BbNAR6Add2nJQlVR2RBZy8GxwyQLqRmb\nGQT/aMQAACAASURBVD0f2w9AcZqAvBx8Rg2DsMcPFKcEqtkJnbH+/ybYcgUdG35ryj46g+10Bqc2\n0ZzsNza9esbbgm5nScOMMffvHMnYk2TGXwAg0vsKmrtfsmB/h0LMpOEzqoVS1m6mUt/J3KrKOzav\n4qU9Ldx5WS9tPg+Gbdd15puLcNBLMuMcbDvf8lYCl2/GGBsFIBV2DhBWTaDa3Rrk7/7wJn7zZRvn\nHBCXdbYEeOcbthPwOGNU1akZVbfbpFg0ObI/imXabLumb9oDRDEXxTZz+EPrUEpnECcHqgBXX7+G\nN7/jxQy0+igE3DSX5qQWLWef3vIc1Wkyqr983ilTjY7PfICrZFQvstLfsUSu0qjrvx8/xVMHo5wc\nSvL0oSj5gsn1W7pnPXCbpUBVDZYC1bb67HtyTOaoCiHEdE6lnM+cdaG5d1Y/lzafh/dtX8ftq5zP\n9csjTRQsm1Op3Kz32z+WqlwuzlK263epeEsn2jv8Xt6wvguX4iyN41EVXrvGOVlZfm4/PzNa6atx\nNHF+8weXW96cvoGRpxTwFcyFrxxyqc53lslzLufTTKlsdbiPy1s2AtWuu2WbIuuBmeepJgpJTpQy\nqdNljou5ERTVw7hZxFJK39HM6u+abdskhndVtl0sLtWF3+UjM485qmbR+Z3v3fZuIj23SJAqlkTD\nB6pFy8alKLjm8Qfhd7t4zZpO+oI+Wkst5sfz85vAH2nyYpg22byBoij4N26q3Ga2O42WyqW/C8Ht\nUumMlMpAlOoBVXHVBKoFk/i4c1BZvW76ko9y2a+vJlC1Zlhgu70rRLpgEvB7CPmc17dgK1iWzQ8f\nd/aTStV/oIwn8+z//9k7zwC5rvrs/26bPrO9S6tVt5otuQM2LmBKwISa4FDCSxIgCaQDSd4kLySk\nUNNDCD2YHgPGBhPAxhjcq2RZXauVtL1Nb7e+H869U3ZmV7vr3bWw5/ki7U65d+7cPec853n+z9/t\nxeoR+XqQAwHkYPBZp6gmsmLn8/Idnbz3jXv56G8/n4/9zvPxu9avy3d0LvTysqLqElWttXpXNJNI\n17ymgQYaaKABSum8GyLBFX3f9oCvVF60rUmMzQsl8Vq2w+FEFr8ss7ctyg1986tbkiSV7L+tfo3u\nkJ8XdosNyh3NYXpCfqKawmAqz3CmwKPTqdLzf1GJ6nypvz53492osyaZzE3XrelcLDxSmDWqv7el\nhClV4vr1VwPQF64OrCzVqSaHmM7P8tjkgarHn5w6hINTdU4eHMfGKE6j+tsxHQvHJe6VimohPYie\nE2KAUZzGcVavdV1ICy0pTMk2c0hKAM2/+PK2Bhp4uvgFIKr2om2/9dDqWljnxsKfC9GQGNRSOfd1\nXWWLq6+3DwAru4Q/8EL+nO1H2qPi65hNl3dnSzWqilBUC+75BEL1B91Ciaj2lxVVZ/7PniuYhPwq\nEZ84tm7BDx4Y4siICHnYf6w6hv2Bp8ZRHNiARDq58I6z2tLyrCOqSZe47xxoZcdAK21NAVpjAX7z\nlTt51QsGWN+5cLDAuRTVfKpBVBtooIEG6sHbcG4PrKxttBIbo0FUSeJYsjy/p3ST/TPlsflUOk/e\nsrm4PcqvbOpmcyy04Ht6gUpt7nlf19vCy9e189J17UiSxNamEFnT4isnx3CA12/sosWvcjKVx1qF\n4KHVRomozg1TclVNvU7y7y3Hv8vnnvoKI5mxZR3TtIUjrVZRXVqNqoc97Tv5nYt+g5dsuLbq9xti\n/ciSzGDiNB+4/8N89uDNjKbLgZ37p58q/X+u9dfUE+BYqH63LEvyxISKHr6umqoFusCxsPTVC1gM\nq8ElhSlZZhZFbbSjaWBtcd4TVd12SraZ5cBTVJdKVGNhMcCmsjqGafOFx8uWzNjABqDa+nsuDP3l\nnzP4x3+AY8+/O9bs/v2PzJbPtVSj6iqqBTfEJxCsHXQdx6GYOYOiRVF9LWWiOk8cvGFamJZNKKAS\ndolqMm/xhdsPofmEQphIFYi7abaO4/DzJ8dYJ8l0ImHMLlzboDa3YOey2MWlhVmdz0hkxGdpjlRP\nwJds7+DVV286pxXGC1OSg/UV1WL62dVPr4EGGmhgpZDUTfyyTECdP7zo6UKTZTbFgkzkdZK6mDv/\n9akzfH1wnGHXDvxUXLipdrUsLvG0UlEFUaJ0dU9LaX2yrUlM/gndZFdLmM2xEP3hIAXLJqWvfPDQ\nasMjhzU1qu6aZK6iatkWJxKnADg0c3RZx/TUy7xZwK5QIQ1LR5WUkjV4sZAkiV1t2wlp1ZsQfsXH\nukgvZ9LDJeU0kU+Wjn109jidQWEXnvs5vSAlyS8ccU5pjSbWFcXMWYqZ0wSimwm17Kx6zWogqIUo\nWvqiwq0cx8Y2cyjqwpsyDTSw0jjviapQVJd/mt7EENeXSFRDZaL6w4fPcDRXHnA7topeWkux/nrK\nol2YX4WMBsSgd3Aoi+3WvJRqVBUT01VUfX4FpU4KsmPr2GYWLdiNJEnI51BUc25KcchfJqonJzLk\niya//EJRn6EAg6NiEB4aTzM7k6PD3eA1zzGBKlHRHmclLdLPNBKuotoUnj/ZdyHMtf7K4TC2Wr63\njCWo9IvFoZmjpPSGUttAAw38YiOhmzT5Vz8D0iOOnqqadXuY5ywL23E4lMgQUhU2RBdnQe4N+ZGA\nvnnmja2xEDKgShIvXy9aoYTcgKbCIvvAn08ozmP9nU9RHc6MlkKQjsweX9YxPbLl4JCrSLIVwU0r\nG0i1uWkAyynnnkznxPru0MxRTMfikq6Lqs6pdI5ezalPENVSeZZbo5p01dRY91VoAZfsrmKdangJ\ngUq2mQcc5Iai2sAa47wnqk9XUW32qUjA7BL7gUbDYgA5PZHm9vtOEw6XwxtCne3IoVBV6u9CqFRR\n7cL8A0LQJ5736LEk//atJynoZlWNql40yeeNumoqiPoBoLTjtVCYEkDOvSahgErInfstZC7c0s41\nFwt7syCqKWxbqKm9lL8Lx1h4ApV8YlJ2nkWKanIeRXWxmGv9lSSJQEd7xeMrq6iOZyf59/2f5W8f\n/MSKvm8DDTTQwFqiaNkULHvF2tIshHKdaq7Keps3bc5mCqQNi53N4UVnZ1ze2cT7Lhootc+bi6Cq\n8JqBTn5lU3dpc91TjfPm0oIgQfRsvePsVCn4bzVw8+Fvcs/w/XUfm79GtVZRdRyHw7PHAJCQOJE8\ntaywJdMpr/EyFXWquqUvuT71XNjk1ql6mM2Lsq79UyIR96KOPSiSUkNUPXXU0USrGUX2gSRjW0X0\n3DiF1HH84fUEIhvQ/KtPVD21eDGBSqX1pdYgqg2sLX4B2tPYaIvsoVoPqiwT09SaMKV7Rx/kzjP3\n8N5L30NQrU0QbHIV1R88eAbLdrjpxVvpu/6P0cfHkf1+lHAYe5GkwoyX+6HZ+QXqOu0iILF5XRtP\nnJjmH25+jLffIKxFimJRKJgUcgbt3dV2o2zBIORXsdyBRPaIqrRwmJKnqAb9KiFNIisOxLvfsBfZ\nsdH8CkrR5I4Hz/CTx0dAt9iNTEt7iNnpHLLtYFo2ap3v5ys/Oob/6Cx7ALv47OkNmsjqSJJIhV4O\n7DlEFcDX14c+JsIT7HyBgm4SqFiM5QomAb+y5DRpKDc/zxjz36vxQoJDM0eZLszykg3Xlf4eHNsG\nSWok+zXQQAPPOBKuK6ppDYhqm1+jxa9yIpWr6qmaMUxG3ECnxdp+ARRJKtWpzodLOpqqfg4qy1dU\nP3tU9Frf3hRm0znqZ5cDy7a4f+xh7h97mBeue17N46dSZwBoruj9CRWKqm3wyMQT3D/6MGczI6W6\n0os6dvHE1EHGs5P0x9Yt6ZwqSWE1UTWWlPi7GGxq2lD183RuFsM2eWrmCG2BFtZFetBktaZG1ShM\ng6RgKm5GhaIhW35su0hq4l4AYl1XAaD4xP1gmavnSFtKL1XLFNdUblh/G1hjnPeKqu3wtBRVgBa/\nSko3MSsi5A/PHGMiNzVv4X7UrVG1bIeNPVGuurCH8O49tLz4BgDkcGTRiqoxVa4xWEhRta0ikuLn\nD39lL9fs7eXMZIbPfO8EIBTVdLqIbTsEKxTV4akMf/AvP+d/Hzpb2vGSFWHnOJeimnbrXaNBDc29\nxFfs7qWnXeyY+f0qXlVHQbfoQ0ICrnjhRiRVRgMy+frv/eNHh0m5D9n6s0dRTaSLNIV9yMu8J70+\nqV4fVYCuN72V/g/8DQCFdIbf/cQ93HbfEEYywWP/+En++sPf4c5Hh5d1vMqJ8mOP/DtfOXJL1eM/\nOn03f3Hf3/GVo7fww9M/4VMHvoBuGZiJOCfe/S5SP7tnWcdtoIEGGlhJJN1Sk3MRvpWAJElsawpT\ntGwenirnU2RNi4PxDH5FZnNsZZOH5yLgEtX807D+TuRXZ5PYdOZXeSdz0xycPszGWD99kerEXC9c\naTI3zRee+ipH4scJKgH2duzhzRe8gXUR4eTKLiHgp3ROdvmcEsVyAJFuG0vqoToXGcPk80dH+O/j\no5xKi/m72d9EW6AchDibS3A8fpKCVeTCjl1IkoQqq1Xk2XEcjMIUmr8Ny/HaLqrISgCzME0u8RRa\nsJtAbAsAkpsI7CyifnS58Hqpzk1KrgePqDbClBpYa5z3RBWgaD09+0qrX8OBUjACQEoXu1Szhfqp\ntF6YEsCbbtheo2Yp4TCOrmPr554IjOmydcPKzz8A23YRWfGjKjJvfel23nj9Fgq6+IpUxSKVEDu5\nldbfR49OYdkOP370LKbhDSSLs/6ms+LcIyENxxKD4fqe8q6u368SUGRaon6u29lFKxItHWEGtrYj\na4KoprI6juPwhTuO8ONHzorPa4pB2PAG2meJ9ddxHJJZnabI8upTofz9y8HyIkeJRgmsW48pKfht\nAwe4/e6j7P+bfyB/94+5aeSH3P2jx5Z1PKOiFuhU6jT3jj7I4Rlhs8qbBX4wdCcRLczrt76KvR17\nOJ4Y5HNP3Ux++CyOrpM+egiAglnAspduQWuggQYaWAl4RHUtrL9Qtv/eP1lO6z+ezJHQTXY0hVGf\nRnbGYhBQxDZxYRnWXw/D2YWT+ZeLeu1lPDw49ggODteuv6rmMZ+7Jvjp8L04OPza9tfxwef/Kb+1\n5y08r/cywppbM7mElikAtmNX14zmyy4242lYfy3b4fPHRjmeynEkkeVLx0fJGOI+vKrvCrY1b0aV\nVWZycYYzwhW1tVm0MtRkrWqj2DJSOLaBFugo/V6V1VJgJkCs88qSg0mSZJDkeddvK4G2oCDbY9mJ\nczwT7JKi2iCqDawtfiGI6tO1+tRL/k0bIlxmJl8mqnmzwJ1n7iFeSBAJauwcaOEVz9vApt5q+woI\nogrUtf/qllGVOjc1cqL8WHb+UBvbKiLLYtCSJImXXN7PO169TxxPtcimXKJaYTs9cHJGfLZUkdFJ\n8X/PmuEFMZ1LUY2FfDiWO8gr5Wvt86s4lsNHf/t5bHZDIK68ZiOSJKEFNBQkEqkCp8bS3LN/lK/+\n+DhHz8SZTIhdR0MS75VNPTuSbPNFE8O0aQ4vf3fWzuWQ/AEkpTaB0FB9+G2d9e0h3ph8gFhinElf\nC0Fb59L8qWUdr2jXbqR849h3MGyTB8YeoWAVuXbdVVy3/iretusmLmjZypPTh/nqYzcDMD0yiOM4\n/M2DH+dDD3582a0DGmiggQaeDhJFT1FdG6K6KVprcRzJiU3XC1pWf7EeVJevqKou2Tm7SkR1oZRY\nL7ivP1pr3fVCjVJ6moDi55KuvVWPh0pW1KURVW8TtdkvNtpnXKJq2RamYy07TGmioDOWK7KjOcwv\nrW+nYNn8cFiss16y4Tp+/+J30hZoYSYfJ+2KH01+ESKpygpmxdrLq0/VAu2l3wtFtUxU/ZH+quNL\nkobjrJ6iuqVZBIMei58853MbimoDzxTOe6L6xs3dvGx9+7mfuADKRLX8B58uKaqz7r9x/uq+v+db\nJ27nrrM/Q5Yk/uSN+3jdNZvrvqfsElUrU10/UDAL/N97P8Tnn/oKACcSpzh1qtwQWp9HUXUcB8cq\nVg1aAJv6xGdXFYu8mzgbLPV41RkaSxFzf56cEZ9FXqSimnIV1VjYh2OKayNVxP6H3MCgfNZgZkoM\nUr3rm4FyH9d4PM/DR8RunAN8+vZDnHR7sHqKaj7zi9m0fC6SFddrubBzuVLi71zk0PDZBi+efZS+\nmVPMtvdze9cLADByeZxlBGPMDaW4pPMiJvPT/Pj0T/n5yAOoksJVfVcAYtL8rT1vZWOsH9z0YSmR\nomgVSRSTTOan+egj/8r9ow8v+TwaaKCBBpaLY8ksj04LC+5aKap+RS6pqr+yqYvKao++0PJdNYvF\ncmtUi5aN6c4V0wWD3NNQZOeDuYC7ppz4W6tiViqbO9q2E1Crr+NSaiarzsclc10hkZg87a7rPOV3\nuYqq58LbEAnwvK5mWv0aB2bTmBUBmc3+JlLFDPGCUN6jmiCqcxVVLxSpWlHVqtZ8ilZdpyzJ6qpa\nf2O+KN3hLgaTQ+dsUWMbXlhng6g2sLY474nqha3RUgrecuERVS9QybAM8qbYaZwpxLFsi88d/HIp\novtM+tz1gL6OLgDSDz1Y9fvx3CQ5M89jkwf4+KP/zj8+9km0RJnMGrn6hfGCTDpVNhAASS6n/not\nazzr71ODszjAtftEXYdpVlt/kWRAml9RzZVrVD1FtVLpizYFWL9ujNnTXyU+lSYa8+NzWwOEXLKW\nSBR4+MgkQb/KK563gdlUka/eedx9vQibKK5Cy5VnAgVdXKNQYPkLJSuXqwpSqkQwFiFiFeg69jC+\n3j4u/cv3877fuhoAxTJKrXGWgkqi2h3u4qYLXkvMF+X7Qz9iPDfJhR27iPrKoSAB1c9vX/R2dgVF\nWEQgWyRbFPdsb7gbVda4+cg3ueX4bUs+lwYaaKCBpcJxHG4dmiRjmuxpjZTm87XAr27q5k/2DLC3\nLUbY3cRVJWlNzsFL/V2q9dezpnoYy6186U0lqZkbGKS7Lp65PVShHKYEsC7SW/N42F27ZM2lubA8\n4hxUgzT5oiVF1evnutwwJc9uHvOpKJLEjuYwuu2UalUBWvxi8/5sWgRYRX2CyM2tUfUUVTXQXrpm\nmqziLcNVX0tNcKEka6tq/QXY1rwZ3TYYSp1d8HmWKZTyRphSA2uN856orgRa51h/00aZLM4U4tw6\neAenUme4rGsfHcE2xjIT51Svmq65FrWtjdn/vYPi6Gjp95W1EYPJ0+xs3kpvOYsBI19/ALZtMZnI\nSnUCsSRJ2I6KopQnK4+oPjkoLCiXbO+kKeIDSwyeTwxmSq+VZG3e1N90Tkwo0ZAPPEW1wvobaw5w\n4a7jOMYZbCtDa0d5Jy0aFYT6oSfHmE0VueyCTl599Ua29DVRdAldR5cYwPXc6tiP1hp5V5EPLnNH\n37Ft7Pz8impbZ3Pp/72/+x7UcJhNA2KHWLNNJmaXTvhLbQJkjZf0X0tQDfKaLa8oWdOv7Lm05jVh\nLcQWrRsAyYHM1DgAW1s286eX/T4t/mZ+cvbnVfb2BhpooIHVwHheJ66b7G6JcNPmnmWlny8XQVWh\nNSDmW4+odgS0NTmHYEWYkmnbHIpnFkVaM4Zrg3XnqdUIVKokYAWzen4vWp6KWUtUK5XNvkh3zePh\nUruUJSqqJYVSoS3YSryYwLKtUr/WeueyGCSL1QFe25vFGuhIojwXh7Q2NHUL04VZ/IqvFNzkpf6K\nEKVpsjOPAxKav610vpqsYurCgab4yvO/B0lWV9X6C7CtRbgGjy9g/3Uch2J2GEWNNIhqA2uO5wRR\njWoKqiSVFFXP9guiluHOM/fQGWrnjdtfw7pIL1kzV5UaVw+y30/nTW8Gy2Lyy/9dIrYeUb123Qv4\n40t+l99ouwF0g0KnGzVupUhNPlBDhI2cqP2bS1QBHElDVcsTVDCkYdsOB0/N0hL1s64jTEdTEEUq\n4jjwuTtOMZ0UA/1CO3KpnI5Pk/H7lLKiqlYrqiVIDq0dZeVtwzrxeWzDorMlyK9ctxlFlvnNG3cS\n8Cn4NYWO9pj72ZY26ZyvyBfFNQoss+G8XSiA41QFKVXB3SzQOrvwdYlJXA6I70BzTMbjSyeq3qLh\nHXt+nSt6LgHgsq597G67gO5QJxe0bK1/rplyLXVxWhDVkBqkPdhKf2xdTVP1BhpooIHVwKG4mK93\nLqEdzGrAcB1Na6Xo+hUZCUFUH5hMcvOJMT5yYIg7R2YW7K2adR/b7LalGV8NRbWCPHkt0Dzolo4q\nKShybQ5DpbI5NxEYKmtUl6eoqrJKW6AN27GZLSSIu2GZT1dR9Uj/QCSIX5Y5msyW13xGF6HgdShK\nD1FftPRaVVZxcLBsi/FjnwWE7VeSlQpireJYBfexcoqwB0lafUV1a/MmYOE6VaMwiW3m8Ec3NtrV\nNbDmeE4QVVmSaParxN16A6/YH8DBQZNVfnP3WwioAdZFhR3FS3BbCJG9+wjv3Uf+6BHSD9wHwHRe\nqJxX9z2PTU0byJ8UQUqFzSJYQGlNkhj5IYVUOWDJtnRmz34fkAi3VYcLADiSD1UtTwyhiI9TYyky\neYM9m1qRJImO5gAhzSRvqBQNh2/cJd5fln0LWn9jbjCT4018FdbfcKRMpmXZqVJUI66ieunmdt53\n0z5C7q5zZ3OQ9960j9973R6CUfF8s7C2qb/TyTz7T6x8k+yC7imqtRPwYmDOintDbWmp+3h+UEwU\noV27Sr+TfeL78dkm4zPLUFTt2sbrkiTxzgvfxl9c8cd1FxMAVqa8UMhNukTVTWSMuA2/M8bq9Xdr\noIEGGgA4lMiiSFKpXvSZgkcAw9ryxv+lQpYk/IpMwbQ4kymrlneOzvKRA0MMpurPB2lXUR2IBJCl\n1VJUy0S5YNYS1fnawWgViqpnma1EQPEjS/Kya1RVWaU9KObXDz7wEf7p8U+J91VrBYDFIGGYSEBM\nU933l9jSFGS2aDBdEOuqnCnWQpqynqhW3kzR3IwO3UjhuGS+fePrgXLtrCqrtA28Gn9kgKbua2uO\nL7s1qsvJp1gsIr4wfZEeTqVOV3UJqEQhLcIcA9GNq3YeDTQwH54TRBWE/Tdn2hQsq6SodgTbAHj9\n1leVdve8ugmv3uBc6LzpTUg+H1Pf+BpWNst0fgYJibaAGCwLJ1zCeEE7yu4YUkAMqNnZcsBSMXsW\ny0gR6bgMf6i2bkOSfSiKsFn6AyrRpkDJ9rtnk/gMHc1BQj6DnKERDqg8cnSKQ0OzSLKGZaQYPfwf\nGIWZ0ns6jkM6pxN1Q5G89jSV1t+Av0zoZcmmrZKoxsTgHPMptMaqJ4GNPTF2DLQSjAhiYxXW1vr7\n+e8f4Z//5wCPHJlc0ff1alSXq6jqEyJ06l79BF8+/D81j3e+6S1Ifj+tL3156XeSJCH5fGiOxdEz\niZrXnPOYlo5m2Ngf/lemv13uoSpL8oI7o1aFopqdLCuqUCaqaf3ZkebcQAMNnJ+IFw3GckU2x4Kl\ndi3PFBR3vPRIy1ogoMgULJuRbIGwqvD+izby0nVtFC2bn43Xb63n1ajGfCodAR8T+SL2ChOdKuvv\nHEW1uABRVSuIar35R5IkwmqI3BL7qJastJLK7vYddAbb6Y+t4+LOC3nJhuu4Zt3zl/R+HlK6SURT\nUCqStLY3ifnvaDKL7TgkDPGYqq4jVpH34H1Ww11vRtouQQuIcMxyjaqGP7yerq1vRdFqQ4pEL1UH\nVrnMZlvzZgzbZCh1pu7jDaLawDOJ5wxRbfF5gUpmqYfqq7e8gvdf+ntc1Xdl6Xk9YRGSNJGbWtT7\nam3ttL3yVVjpNMl7fsp0fpYmf6wUGpA/eRwlEqUzdBbtmnbkkBhwcvHDWG54k+W2yvEFOuseQ5J9\naKoFOHT2xpAkiQMnZ1BkiZ0Dwi7SHgsQ0gzyuso7XrULCfjyj45h6oLcmIVpjjz+GdJuSnG+aGFa\njqhPhVIda2WYkm2U621jLb4qRTUU8SPLEunk/GppMOK28FnjPqoj04JAff2u45hPo1n6XJRrVJe3\nYDImBVEd1NLcN/YQx+ODVY83v/Batvzbf6K1d1T9Xvb7CSs2pyfSxNNLu5a6pbNpWMeZmmHyrp9w\n74GR0u5sZv8TTN/67bq7tVYmg+MuJMxZoU579UMRNyxiqfasBhpooIGloGT7bX5mbb8Ab93ay66W\nMC/orlUCVwtBRSahm8R1k76wH78ic01PK91BHydS+bo1qxn3dxFNpSvoQ7cdEvrK1jlWEtV61t96\nQUoAMV+El224nndf9JvzvndICy25PU2llXYg1s//e977eN+l7+E3dr+ZX9788lLbmqXAdhySulnT\nDqlcp5oloZslS7iitOFXy/eG6rqVdEOElMha2RFgVoUpzQ9JcoUEZ5Xtv26daj37r+NYFDOnUf2t\nqL6lX8cGGni6eO4Q1YrkX8/62xZooT9W3eur2d+ELMnMFurvVtZD7AWisXX26CESxWRJqTXiccyZ\nGQJbtpSeK0kSjuWAZJM49GMALNdCqWj1J2PZHfQVxaKzJ0oyqzM0nmbruiaCrrrX0SQjy2A4fvZs\nauOavb2MzeSwXKV0ttBKU6DIxJhQctOuHSgW8mFbRfK9h9Fu7AalvHNoFsr22YHN1Yl0siwRiflJ\nJ+dXS8Mxl6jqa0tUm922OjOpIiNTK0em8u5k/3QV1WRUTGDfOnFbTSBR3V1mv5+gLJ534OTSLM1F\nS2f7afEdqfkMt3/rPv71lidJZXVmbv02s7fdihmfrXmdlc1gdohJV46LiTY4V1FtENUGGmhgFXEo\nkUUCdqxB39JzYX0kwJu29K6pshuoyIxYFy47l3a0RLAch6emUjWv8cKUIppCe0DMhZU95FcC1WFK\nc4iqPb+iKkkSN25+GTvats373mGXqC4lrK8yTGmlkDUtLMepIapRTaUv5Gcok+dMxrUoO+Jfk/Im\ns2dzNr31XUVbl0pivRAk9/HVbFEDsLV5IxISxxK1RFXPjuLYekNNbeAZw3OGqLa65GK2gqhW7zEf\nGQAAIABJREFUtuXwoMgKLf7mUrz5YqA2NaN1dZM/fhxsm7agUDkLbn1qcPOWqudbT4nJpVAQg4Jl\nekQ1Sj0obssaVbVo6whzcI7t17Z02iOCjEiKCDB67TWbCQdUvnlgN5Gu67lzUNQ92tmjAKSzbmua\nkIaeHQHFRukPkdEfLx3XqCCq/Ztqd9KiTQFyWZ1iweDbNz/O7V/fz+RYeeIMuxYZ1pioZvPlQT1T\nWLkJuuCGKT0dRdWRIBlRUCWFM+kRHpl4YsHX2I5NQbZQjQJvHr6D2Tu+v6Rj2rks/WM6lvunfqVv\nlidOTPPBT99LYVjE0R9++HCV8mwXizi6zozjIxuQ8afFvTXX+nsuRTV1/73oUytrv26ggQbOH4zn\ninzu6DCfOTLMF4+N8LWTYyvWDiVnWgyl86wPB4iuod32fIKX/AvQFy63rtvhzq2HZ9I1r5nIF/HL\nMmFVIVRqcbOy1tFq6295s9pxHHTLWHbKLkBYC+Lg1BDghc+nHKa0UhjNiuM3+2qDmLY3h7EduHc8\n4T5HlIoljFjpOZ5a6q3v5AqiaiyaqLqK6ioHKoW0EOuivQwlz5SSkj0UMq7tN9Igqg08M3jOENWW\nihY149kJ/IqPmK8+MWwLtJDU0zV/sJXwBmQPwW3boFikI24yEFsPUApS8m/aVPVaeyiHPZLH0lKY\neqJk/Z2PqKpuU2xVsehZ11SuT93chp6fYPjJj2LM3AnAdpcUR4Ia113cx+HxGIdnNjM4qTCajOC3\nh7HMHGenxODZHPFTzJWDo4rGELY7iHrnBeD31yp9XirwQ/ecYnw4ydlTcW754mP88DtPMXI6jmmC\njYRkrO4gOxeV5DSbX0Gi6oUp+VUc26Y4OkLqvnuZ/MrNJH5y5zlfr09MYDdFsRSJF2+4FlVWufXk\nHVW9TufiwPgRpq00FIusK0yx6+S9fPyh/+JUsn4tyVz4plMoDjwZ24Qjy+yaOMCv7/QRTk0huU3L\n77vrcd77yfv43v1D2I6D5drDZ0yVVFgmmrORbKccpuRafzML1Kga01OMf/bTzN5266LOs4FfLDiO\nQ3zkRxQyp5/pU2ngGcST8QwnUnkG03mOJnMcmM3wxWOjNb08l4MjiSwOsPM8UFOfKeTdDURNltgS\nK1tH29zwwsScjdiUbjJdMBiIBpAliYBLdAvW0nqxnguGU36/YgWhNGwTB2fZKbsAIbf9yVLqVCvD\nlFYCjuNw16gQK/a21a7LvDrVEXdTZn3ExrSmmNV9TLpuNe9cbPdzKFVEVXxv57T+rpGiCqJO1XQs\nBpNDVb/36lP90YFVP4cGGqiHZzVRtWyL/zzweb5x7DvENEG0Zos6E7kp+iI9yFL9j+8pogvZf386\nfB/v+9n/YzApFmqhbRcA0D+us71FkMXCyeOgKPjWl2PYHdvBntGxjgoykJ190iWE0rz9qVRNEEK9\nN0Ag5OMpty1NX3uYzPSj4FjoObGj19LSV3rd1ReKYKYfPHiGfNHk6FQrkuRQzAxz56PD+BSH3W37\n3f5eYJ3M4jgGmbgYmLydQACnjg3HI6oHHxtF8ym89DW76OyJcvLIFN/96n6+8blH0GUN2Vz51MH5\nYFp2qY8rQGYFiarXnsavSpz+wF9y+q/+L+Of+zSJu37M5Ne+gmPPv2ttFwpYyQS622JhY6yf69df\nTaKY5M4zP5v3dYemjmGo1ZsEg+njfOzRf+OT+z/H2fTC6dS+hCCTE/42zJe/Hjufp/eOL/K2aDks\n7MKYgW7Y3PLTQR45MomVdeuYpRCpsIJii/tanRB/D2Xr7/ypv1ZOTM7G9MqnLzfwzMPU46Qn7ycz\n9fAzfSoNPINIuJbSP9qzgQ9cvJkb+tpIGea8QT+VMO2FA37Ol7Y0zyQubY/REdB4985+NLm8Xgko\nMookkZpj6R1Mi3F3YzRUeh5AYQWzGmD+MCVv03W+GtXFwMtC+PcnPsvwOeY3DyWFUlo+UdUtm2Nu\n25mjyRxnswV2tYTpC9cmBleq2xe3R9nR0YdpnMRB4p8OnubjB4YYzncjSSEsj6hqtdbfyhTkepDc\nz7PaNapQ7qd6MnGq9DvbNihmh9GC3SiN/qkNPEN4VhPVx6ee5Mnpw/x0+D4+e/DzBBSJqXwB27Hp\nrdPDy0Ob289qZgGiet/YQxi2yVeP3IJlWwR378ZUJC48WaTd34qt6xROnybQvwFkMSgVh3MYt40T\nXLcFORXGMR2yswewjAyKFkGSZNKPPIQ+PlZ1LEV1W8hIBoOjKbIFU9h+HYts/GDVc7VgOZCpoznI\njg0tpXChqYwYaIbHzjI6neXFF8kYyccw9TiYMtZBYdtNTh3Csa3STiBQt+l0tCLtd8+lfWza3sFr\n33oxL32NsBkXCyZ5XwzZXDtF1VNQQ67V2yOqtlUkMXY3plFrlVosPEVVnplEHx3Bv349Hb/2ZgJb\ntoJlYWcXUBjd1jQ5t61Pkz/GSzZcR0QLc9fZe+Z93eGpE5hKNVENj7ayMTrAwZkj3PLtj3Lo5k/O\nG1/vS4rvMKFG6H/pi+l7zx+ALGM+tb/0nD4nzZ+9aR+dxVmmv/Mtxv7rk+JcCZEJi3vv1XcnGf3r\nD3Dm7z8EjxxAMR2yRo60nuGxyQM1x3VcFd373A08u2C7dj9riemcDTy7EC8aSAh7pE+RuaxDWB9n\nXKXv/okE947HsdzxyXYcDNvmh8PT/PVjJ5kp1N/EdByHE6kc7QGtVGf5XMS+9hh/uGeAjmD1NZAk\niYiqkJoTknQqLWolN8WE+2WtiWrRqm2HtlQMxPqRkJjMT/PRR/6Vu87+7Jz1qitRo/rTsThfODbK\n0WSWH4/MIAEv6m2r+1xZknhxXyvrwn5esb6DF216AX9x2Y28ekMnO5rDpA2TiWIzft8eHHeMfDrW\nX3sNFNVWt1NFZfZEMXMGHKtRn9rAM4pnNVH9ydmfIyFxQctWjicGKZpxkrqnivXztZNj6HUG8Da3\nD1e9OtVj8ZN8+sn/ZiQzhizJjGbH+fGZnzLqJDm80U80Y5J9/DGKp4fAsghs3lLeURsvYg/nCe3Y\nib+zD/tUFrM4g6UnUNQIZirF2H/+B2f+/kMUR8uKlywLcuPYOoeGxDnt2dRGLnlENIuWxOCsaE3I\nir/qfL06VoCZrJi8RsaHAbh0W0V6oSlhj+aRJI3E1KEqNVUcvNY65CmqPr/C3suF3VmSJDZt72Dv\nFeLngi+KahkrHo8/HzIFMaB3tojP6hHV1MTPSY3f87QUoHzRwq8pFN1+p03XXk/L9S/G3ydUbDNV\nG2zhwSOxWZ+4DjFfjKAaYH20j5yZr2sz1y2DE7NDNYpq01iYvdKNvHPnm7nmoSTq3Q9y6md3VB9P\n15m+9du0jrv1z23tREM+wnsuZP17/wwlFkOJRvH19qIPn8X4pw/x9rO3s+XkgxgzM8T7tnEwupl0\nZdx+axuFwZNMf+Hz3PTDOBk9w4ce/DifPXgzZ9LDVcd3dLFgMePxBZXmBn4x4ZiCqNrW0vodNvDs\nQlw3iWkqqtu+I6QqyJLo5Vm0bG4/M8X3zk7z6SPDzBYNbjk1wQcfO8ndY3FMV7mqh4xpodsOXcHn\nLkk9FyKaQqpoVG1SDqby+BWZnpBYB3hhTPlVrFGttP7W69u9VFzSdRGfuOZDvOvCtxFQA9xy/Db+\nY//nSBbn32ReiRrVY0kxR992ZorRXJELW6N0h/zzPv/63jZ+Z2c/QVVBlmVaA81c3tnEW7b28ge7\nNwAgSSGw8oCErAQrzneRqb8l6+/qb/b73bVjZSlSoy1NA+cDnrVENVFMMpQ6wwWtW/ndvb/Bi/pf\niG7GsZGRpCCPxZs4MJvh0elaclFWVGuJ6m2DP+CJKaFivnbLK4n5otwx9GN+dPpuHrsghCNJTH/r\nf8gdOQxAcMuWkjJpGGKyCO3cha+vt2T/BZC1CFZaDMR2NsvwJz6KMSVa5EjeoG/rDLu1pRt7omRn\nRBBPU/fVQLWa6mHHhpbS/2dzglhKVoJt65vpiJUHSeV0BGzwBzeg52cppIfEebm7gE4dotreFaGl\nPcSV127CH6i2sERc5VD3RdAcs9TaZbXhKardraHSz5aRIT31kDif/ASOYxMf+RH5VG3C3UIo6CYB\nv1IOydokLN5KTARNWankvK+1XKKa1mwkJKJunadnc6oXTHQ6dQbLtmqIanvK4N6D42wcLhAuiHsq\n/u1vYxjlBUPq/nuZve1WekfE+7b3lx0EgYEBBv767+j/iw8Q2LQZxzQxkwkym3bzna4XMvq2P+OR\ni24kpUVIyeX7p+ddv8PA330Y//r1tCVMctkUGfe8i3Ps3aWWRJZV97roRZP4dCM1+BcVnqJqNxTV\n5yws2yGlmzRXpKDLkkRUVUkbJsPZAg4QURXOZAr888HTPD6TRq1INj+dqb/REXctrS11gmwaEIho\nCobtUHQ3ApO6wUzRYCASKPV8Da5Sjeo5rb9PI0wJwKdo7GnfyZ9f/kfsbN3O4dlj/N1Dn+B06uyC\n56NK9RVV03b4/NER7p+o34s8Z1qMuvWm8aKJDLyor3XZ5++Ff8lSCMkqIKvhqkT/RSuqpfY0q79+\n8uzale2GipkhkGT84X5AiD9/cs9f8ZUj/1N1DzTQwGriGSGq27dvv2L79u13r+YxkkVBQHvCXciS\nzGu3vJJtzd0AKFI5me1IonaxXFJU61h/p12VdVvLFq7suZTXb30Vhm3y+NSTFFoiNF13Pcb0BMnR\nu5E3hAhsKhPVB7cEOHD9ZoKbt+Dr7cM+m0OyxUClalFst65P6+jESiQY/sRHMBMJZHfQlzAYm83h\n9ylE/QUK6UF84XVE2y9DC3QSat5Zc77ru8qKmKRoJPN+2kJ5XnrZemx3QGobeC1SXAzwwYioU8jM\nPCrOy+2b5di1E53Pr/LG37ycXfv6ah4Lu0S1qEXQbJNMbm3qVLN5A0WyWddqAg7Zgklq4t7SjqSR\nH6eYOU168n6mTn55Se+d1y2CPpX84AnkQACfq6SqMXE/LaSoenWfKcUg5ouW6qPLRLV2wX/CrRUJ\nhqrDHC5LD3PNw19n7DP/Jd6zp5lo2mBi6Ej5OjxRTm/OBBSa59R5KZEIWlsbHa//Vdb98fvY/I//\nysC7383R6AD3H4+TyIh7Y8Ysq+6BgY34OjrxbxgQnylZnvS9nXQPnqIKYMzWbvg88NNBvvH5R8hl\n165+uYGVQ5moNhTV5yqSholDOajQQ9SnkDYshrPiHrlxQwdv2NiFjIQiwW/vXM/fXrqFiKpwOp2v\nW7bgtVNpDTSI6nyIuGTIa0czmPJsv+VawjWx/pora/2tRJM/ym9f9H94zZZXkDGy3HmmfpnMucKU\nTqVzHE/l6goTAIOpHA4itAqE5frpWM4VWUKTbSQphGQXq4KUAFK6OI9zXae1DFMqE9WKubswjebv\nKLVIPJE4Rd4scO/oQ+fsWNBAAyuFNSeq27dvfx/wGaC2Qn0F4bWgqUz23dMudoWu7X8Z3t7WiVSu\npk4m5ouiymqN9bdgFkjpaS5o2crv73sHQTXAxZ0XsqtNBCnt7dhN+6tfh++V/aiXt6C9rBNCdsn6\nOxPSGHTttv6ePrBBnhWXQdYiWDlBmpteeC2tr3wVxtQUw//4MRxdTDKSrTMxm6OnNUQuLuoCI617\nkdUgPTveRaTtoprrIEsSm3oFkdra18RMLkBTUGfPxhiOLSYYWfbhuDuu/uhmQELPCiun6hPnu9Qd\nPY+o6loIBYdCfm1a1GQKBi/eNsTu8K28+6rH0JwZ0tOPoPiaCUQ3YRnpmrrexaJQNInJBsb4OIGN\nm5DccAvFJaqecph+6EEG3/tH6GPlIAjP+jst5Wnyl+/JsHpuotrVXL0REMnG6SrOkIu203HTmzB2\niFTp1JhIArYLBXKHD5Web0kSkWD9BZ8SiRDasRPZ56M1FmB7fzPHh5OluuYZs5OcX+KpTYHS51Wb\nxT0RyZcXP3NbCdgVRNWsU6c6M5nFthxmV7DPbQNrB2+Ty3FM7DWwpTVw/mE+1TOqqViOwzHX1rs+\nHHBrLTfwnl0b6Ar6kSSJDdEgKcMiXsdtM+v+rqGozo+Ia+v1iGqpPjVatpj6V4m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6/8BW5ZpoI6H+uCPkQBzubqbYx6frz6H8eqUwxrYZmFhsTfWpRJdkqrLr5De66h92O/6bYyoaqC\n1kKsCdNR/FU1N/WDh7ELedruvqcpwW1FVEU5hOANUFVFtbn1txKmtIIaVUHuRpYtbJ+KqLcmqtm9\nL6CdGV7y+VtBNywCinedmoU2NFRX1xHwyRXrL4Aa7HefV2g9UZiWzYvHpoiFVNZ51l9RaZz05LYq\n+Z+vqEZuvJmZ97+FVwbdYyLx6kDvDyjsvr6fYMg9p2S6E8W2xOa6c8wliwyfmOHE4UnmIxJUccwq\nUR316lNjUiflu+LowUaC2wp6yUQrGETjfvwRd+FYAHrbg+SKBqfOuxsRWtFgNO9Hl4OImfr7z3aq\nQ0sq0IuNQOj9v8KIN5+ZqljZRQ4EVW66cxO2aXNTUEUezXKo5w5E22Lu8ceWfN1reHVg1xBVSQ5i\nmfm1MI3LGGN5jZAscVdvgmTJIG9abAz7m6o8QVlCwLVUwoVXVPtCPtYFfeyKt55H17A8tPkvAlG1\nrYaepR/e9XP83nW/VWntd7HQF+6t/PvW3hvY07mr5bEHZ925aleiednMxUAxcwqAQHRL08cLR48g\neRkOtiRRHDrV8lyCINC97VcIt7v9ao3i6gMGHS9Pwh/ZhC+8sf5BT8EV517G0KarGSbUhCnVENVy\n6Oia9XcNFxOXPFEFuKpjZ+XfYSWEJEgcmT3G3on9nMuO4pNUdtccs1Koksi6oJ/xQqmu55iWc0mU\n7CmRpt6o8rh9p/KV5sjN4Jf9BORAnaIqiCKR629E7vDqEVtYf61TbkCUL+QqsFY2y9wPHkaKRIm/\n+aeavp4UcgdnO1+fbCsIAorffb2yooplIjQJv3AXK+LyalT1NKIUQPQsnI5fQjSa98W0dZ3zf/1X\nnP/bv171Yrdk2ASUqqJqZTOYszOVx+cTVV/IrQ8u5UdbnvOV00nymsmNO7pxTPfczRXVquo/X1EV\nBIHc1nUIlrv4isbr4/Fve8sWfv5jbn2NZCl0BzvpDdWHbM3NukprIVcfWgRu+q9PdM9ZMIqVICWr\nEK7z9tt268/XcRwmxtIMHZvi4Ivu5xGJBwjH/JzFZgyHN13bzztv20jOcO8FrWiQKriLWaFY32je\nwb2XsrEpimqUg3s+xHdeMJgaz6JJAqIqYTvV39iOPb109UbQCt73F+nEQiS1/0DLa17Da4PaNEhR\nDoFj4djzUp8zQ8ye/TZ6YZzp018nM/Xcos6FNbz6yBkmc7pJf8jHW/s7+ODmHtp8Ctd3Nq8xlASB\nUI2N8kITVUUU+a1d61sm2K5h+QhIIn5JJKldSKJqosxTC+O+GBuiAxfsNVohqobpDLTTFejg3Vvf\n0fK4omlxPF2gK6BW+v8uF7ZtMHH875g4/ncU00tzJWkLEFXb0CmePIHi94LoJBltaKjhuDKMZBJF\n6iAQc7McDG31uQ22XUL2ddC15RcQ56UzC167OV/+DACyUh0Hyu1pgnJNjaq3KTG7RlTXcBFxyRPV\nhC/Ojd3V5FRRELEcC902+OKRf+Z8fpKByLqGfqkrxYawH9uBkXxVAdSyLlENtbkx7M0UVbfnlLOg\nogquqprSGu0bvr51SPE4/i2Ng5ugqhg/mCKq34XkBUUlH34IW9Noe/u9Lftsii0UVagm/4rSwtZf\n8OpUl1ij6tgWZimJ4u9A8hL4bEVBakFUrUwaHAdjcgJ9tDVhXApKNYqqo7nXW2sdDfhkDNPG9DYh\nXBu1gJ5vrag+d8Tt+Xnzrm4cwyOqTWLuRUWtKKlSpHH31nBM1JK7CRGNNfZxk2QRW7CQTJmt82y/\nAHNJlxzkc80/x5BaTuYrVlopzU2p1E5D5XPMh+M4PPHQcb715f088m9H2PvUGQDaO0NEggpTQB5I\nRHzcd/sgsaj7vY6Npilv0gt6CKvmHrE9ojq57iSCAMm8QFdvhLe8cwfDigA9J/n445+sJHSLosCd\nd28j3uYS3oBPYcqXwD4/im1cuAXWGlaPqqIaqIx31rzUx+mhr5BPHmRq6KsU08eYG3uE8cOfZfLE\nlzC0qiqgZc+gF6suAVNPo2XPMJvWeGzfaCXgZw0XDvtnMjwyOkPJstnvKU7rvN6SV7VF+L3dG9ke\nb61AReuI6iUfc/ETD0EQaPMpJEvGBfs9za9RfTUhCAL/6fp/xydu+Dh+uXVP1MOpHJbjcHVbpGUN\n6GIwtGn0wjh6YZy58cXdPY5touWGkX0dyL7GzRZtaAjHMPDFXHedEwiitVBUS+NjDH/iPzD5lX9E\nCbhtGZeSqbEYbKvUQFDLKK8HAUQ5SKL/nsr/K2FKNYqqIinEfbE16+8aLioueaL63m3vbCjMXz+v\nRcz6SP8Fe71yDH85UMK2DUr5UZRAD2rArSdspqjai/RQLSPhj6NZpcqPvgwpHGbzn32W+J1vbHiO\n6POBDULJnWTM9BxzP3wUOZFoWptaOecCRLVcpyqIKo5tg+NAM+svbvLvUhVVU08CDnItUfUpyEaj\nEghgZqqkP7v3hSW9Rito2XMMtnnfjeaSUatGTQ763Ym1XKcqSj4Ufyd6YRzHaYzuL5ZMDpycobst\nyMaeSJWoNlFUoRqoJIUb1QjTNlFLAURZIBBqElolCEiqgGQp3Nx7Q8Pjc0n3ftFLFobe+F1EPaKa\n1/OMZc8T98U4O64TrOmfOjNRn3BcyOvMTGY5+vJ5jr08QXtniNvesoW3vHMH9/3c1ey8uo9ITcBW\nIuL23G1PuK91+mRNo287WteipqyoloJZ1t/Yx7s+dC3v/qXr2LqzG82w0dqOek3iX6k8p6M7wgc/\nehOJ9iBWyWQy0IFg20webW2NWsOrj9o0yPLGWas6VdssIAgyif57UEP9lPLnyCdfBtz04OnT/8zk\niS9Qyo0AMHv2O0yd+jJP7T/BV35wgnOTS2sfdbnjVKbA3x4bRVugFdWFwFMTKe4fnuSJ8yn+5/7T\nPDTiOk7WhZpvdjbDrFef2ulX6FqhUrWGVxdtPgXTcSpumNXCJaoXLzBpMYSUIIEFSCrAAW8TZk97\n8/rVpaC2J7yhTWNosws6v7TcWRzbIBDd3PTxwtEjAPi8tYI8sAF94jxWrt715tg2U//0jwBkn30G\nSYkhiGrdJt9K4DgWOBaC2Pz3LtUk+sb73owarGZo5Ax33J//ubf720hpc5XeumtYw4XGJU9U93Re\n2fC3D+38AB+58hcr/1+ot+lyUd5ZLodF6PkRcCz8kY1IqrsLZjWxsJXJ62KKarl+Y6aYWvI1CV6N\narkfavLB7+LoOm33vrNpvWQZUiX1t4miWrb+yn4c0wsYakFUEeQlE1VDc8mL4mtHVt3P0lElZLOV\nolpLVF9csf3XMvI4s/9G2KdjPJ8E0z2Pna+qiJVeqlpNnWqoH8cxMYqNtZ8vnZhGN21u3tmNIAgV\notrqMy/3Uh2ZsfnS557mlZfGKu/HJapBghG55e5uNBgkKsabpufVqqGFfONnGQu43/VkYYa0nqEn\n0M1UqkjMX931nppHVJ/43nHu/+I+nnz4JD6/zN3vvpLd1/ezdWc3fevjiKJApKa1TcKrV00k3O91\nZtz77hwHy4lWalgc28YWZHBsHMHGCDh097nk3bSqijbAyVSj7SkS96OXLAaucX/7P3zgWUoXaIG1\nhtXDdY/g2fu9zbB5RFWUqrvuaqifSOcNtG/4GfdYT321zDyObeDYhqu8Zk5Ryp0BHHyO666o/a2+\nnvGF42MMZ4scSuYWP3iFePJ8iu+NzBBVJK6IBZEFgWvaI9zSFWdrtHXJynzcMeDOHR/Y3Iu0QqVq\nDa8u2rxApdkLVKdqOtZrpqguBRndZDhbZEPYT2IV/XjL7ev8EZd4nj/6eaZO/SNOC1JWtf1urfyt\nOHQKI+Wu9wpHj4Ao4m/3NrW99oSlkXOV421NY/wv/g/FE8er15FJo/g7MbSZlmGeS4HtbSaLksqh\nZJY/OnCa6WJ1g7lIGNsLQqxtg/jk2LMcmD5EVI1U2j+W0RFow8GptJK82HAch8nC6mt113D54JIn\nqs3QG+rm6q6rKhbJwejy0n0XQlSRUEWBGa+XqpY9A4A/PIjsEVXTaFRUy3HfarC34bFadAfdMKTJ\nwtItHGVrr6OX3H6UP34CpbOT2G13LPw8r0bVyjcufoKx7cR630SobQ+Ot4svtEhwdBXVpS0YDc3d\noVf8Hcjl5tuKhGI1V1TLRFVQVc/+O7Kk15mP1NgjCE6JHx4ZwNo7B94CqlZNDvrqFVWorVNttP8+\nd8QlrzfvcutFF1NU2+55O+0/8y7OJy2KBYMnHznJg/cfIp8toWs2kqUQirbeWPD5FcxS80koXUNU\n89nGzzIeCuLYIuN5tz5VKLn3asCzxCuqxJH944yfcycTx3E4O+RuKti2w5vfsYNoPNBw3rKiKgqC\nmy4MdLWXVTSXcMaLEziCwlza/az1UhFLVBAxQYBcqeoe0Dw1WHTcz/Bo8kSF4JZRtkZv2O3Wnftn\nxvjH7x9fC+y5RGB7bhBJrrH+ziOqDtXvyhca8I73xiPTHY/KG35KoBvH1pke+ip4z4spruVe+wnb\noPBfpJYsT06keGh0hqgi82vb+/nQ1j7+4NpNvHdTD+/Y0IksLv1137tjHf/ftZvpDS5dhV3Da4v2\nCxioZDs2tmM3hCldCjicyvHpA6d5bHwWh9WpqVAt8wp3XFf5Wyl3luTI99xcEtuobM6D15ZGVPCF\nvSyRXI6RP/kjpv/lq1iFAtrwafyDm5C9NYTQ6Vp6S17yrzk3x8if/BH5lw8S3HUlibfeDYB25oxn\n/7UxSjVOpmXC8VqLCaKPR0ZnyRoWL06nKVk23zozyf0zA+x33HlX8Yjqo+d+xNeOf4uIEua3r/61\nBodjOfl3oTrVx8eTfOH4aCWEbTV4YvRp/vC5P+XA1KFVn2sNlwcuS6Jaxm/s+RV+/8b/QGdw+d0W\nTSNLYe5Yw+JXEAQ6/SqzmlvP4danCvjC6xElH4LkwyhOouXO1T2vlHcJli+0sA25HJIzkW9U8FpB\nUD0LrV4i/dSPcUyTtnvf2ZJYllFOAm5m/RVEiVjP7W7P1zJRbWX9XUaNqllyiars76jUQQiqgOJY\nFeW27viMS/ojN90MQPbF5dt/i5khCqlDGGInh0+7oUZldbOWpFcU1VqiWkn+ra+PTedKHDmTZLA3\nSrdndS0r2tNpq2mtaGDzFtrvfSeZOZd49W9MMHI6yb/8/YuUxt2fWjjWenHn88tYloM5rzF7ajaP\nVqxec+1rv/jUGe7/4l564wEwlQpBOHHCRpVFRNsmlgjw1p/ZhePAg/e/zMhwshLONLi1gw985EY2\nbG7+GwoH3Qk1HlErfVh7u6v1a6pPIlFyScX4Gfe7zOVSWIKMgHvN+TqiaoJoYgvugqlgFvn9pz/F\nN05+h6mCe+9EYl6DcSWC4POx3krx7OEJfvjSAjH+a3jVYFX66wUrRNWuqVF1bLPSwgbAFx7wjlcR\nRBXL8IiqtwgMte2hbX01FEWQfHT4phBwfuKU9IuhUJYsm0dGZ4goEh/Zvo4Ov4ogCIgrfC1RENZ6\nnF5maLtALWpM2+SvDn4R4KL2Sl0pHh6dIWNYvDidQQSuXGXabzkATg300LX1l+je+mGUQC/55AFy\nM/uYOvVlzh/9PLm5M8wMfwOzNIu/pi2NPjkBloV+/ryrkDoOwR07kb3fntjuuhP0sTFKoyOc+9Qf\nUjp3lugdb2Ddv/tdAtt3AKCdGUbxe3Wq8+y/pp4mee5BMpNPU8wMLdjX2vZC704bbRV1ff9sls+9\ncpYXp933Ouu4ayhRDvPI2cf51qkHifti/PtrP8a6cKMQU+6lOqM1J9Bns0UeHZvlVKZIapX3X9HU\neOjMowA8c/7FVZ1rDZcPLuvZxiep9IV7Fj+wCdLjjzMz/HVSo99rIKudfhXTcUgW8+iFcdTQukqR\nuSj6sYwsUye/RGbqeRzHwXEc9PwosppoaKA8Hz0hd7A5n1+GolomqiUdY9IlBcHti6ccC7KM6Pc3\ntf7WwvRsKWKwuW1ZWJb1dwYEEVmNV6LNBdkdlO1iseH4sqIau/UOBFUlu2959taelPoAACAASURB\nVF/bNkiNfA8QSIl3oHrKr+JNAHahifW3hqjK/g4E0degqO47MY3jVNVUcBVVTQryvUcnePKRky2v\nKZvW8AcV7n3/bm66c5CSZqIdd8nXYkQVQPfsjpZl8/QPT/GtL+8HqJDJ2uTfvU+dYWYyhzVTqLSo\nAcinAtxz03pKRZNQxMf6TW289Wd2YdsO37v/EC96gUn9gwkS7a1tfxGPqCbC1etuq1FeY4kg3dYI\ngmPxygsTGKZJKjuDJcqAe8/kjZpgMt1CUN3/39B9LfcOvhWfpPD4yFP84XN/yl8d/ALnLPfacpkS\n/g0biRVTtPkcvvbYSU6NrvVVfa1hWzWpv0pZUa3+zsrqqhrqJ973FvyRajiYpIQrRLVcLiErUcLt\nV9O+8V3E+95MMHYFPlmnI1Sk1KQe+/UM3W6slV8JHMdhVtMxbYezuSKWA9e2R9f6k/6EokJUV5n8\nO56f4EjStaReijWJvhpnwNZYcNVhX+XNNEmJ4A9vwBceoHPTexHlIKnR76N7HQNOH/wyhTm3/tRf\nY/s1ptx1njk7Q+HIYQCCO3che5u+xOIgimSef5aRP/4UZjJJx7veQ/eHPowgy/gHBwHQhodRA64b\nb36gUnLke+Rm9zE3/hjTQ19h7NCfkZt5qen7KbemGdJdpXl92E/etEjrJnf1JpAEh6zjjukODg8N\nP0pEDfPvr/0Nur2163yUW9TMNilnM2ybfz0zWfHX1NqMV4Knx58nbxQQBZGjyROVMMY1vL5xWRPV\n1aCUdxXR3Mw+Zobvr6gEAB1eQMR4ahxw8IcHK49JNf78ubGHGT30J0we/ztsS0NdRE0FiKlRArJ/\nmYqq1+tUL2HMTIMkISeWFt8vhkKURs4x8sefqiiC81E87dZV+Dc1ps26F7A066/jOBjaLIqvHUEQ\nK82kJdkdpvQmhLkcpqR0dhDeczXG5GRdvcZiyEw8iamniHTdRM5MoHqTp9zukrp6RdXdAa61/gqC\ngC/Uh1marbsHJr3woi3rYtX3Zxikgr04DpwfmasQ6rlkganz7vuwbYdsWiMac/sQbr/K3UhxCu6E\nGUs02mvLUMtEumDgOA4/fvgEjz14FNt2uPPubVx3m2txLyuqtYT+3NEpRN2ziNsi7f42bt/hTixh\nr7Z0cFsHb3/vbiRZZOiYuytbrh1thUTYRzigsLGnelwgWCXEckBGFGT6ssdxChJfefhBJh9+AEuU\nEXAX3fl51t8yUe0OdnDP4Fv4w1s/yYd3fpCN0QFemT3Gj2Z/BMDY2RS+jYPgOPzqdVEcB/7mgcML\nXu8aLj5c66+IIKpVRbXG+lsmor5QP9HuWysN48Gt4bfNPI5jV6y/kureW6HElUS7b0MJuJtDneEC\nJePCELdLGVqNg8JcoIXUcs7318dG+cyhszw6NstQxv39bYq2HnvW8PpGTJWRhNUrqulSNVPijQML\nlx41g14yF2yTtho4jsO0Vl3jXNOx8Ny2FFhG1us7X1WPZTVOx8Z3Q015g+F9LvF1byXctqfyd33K\nXefZmkb2pb0Iqkpg0+aKc8KWJNSubpxSCbtYpOdXPkLbT99bybGQI1HUnl6KJ44hyS4hrG1Rk08e\nQsucxBdeT8fg+4h23w5QIc3zUVZUpwwfiijwvk09XN8R5WM7BnhrfwdxRSSLO6aP5SbQbYPtiW0V\ne28zlB+bKTYqqo+PJ5nWDDq9DbLa72cl2Dd5EFEQeduGN2I7NvumDq7qfGu4PPATSVTdnqcFBMmH\nL7yBYvoYE8f+umLn7fTqOSazrufeH9lYeW6i/x7ifW+m54qPEozvRFIilfYKrRo810IQBHqC3UwV\nZ5a8I1muUbVLJYzpGZT2DoQl1hSZs+7gUTx5gtxLe5seU45HD2xufv2CuLTUX9vM4dilSr9ZwSOq\nouwaUkvZRqJqedZfKRwhfL2bdpvbuzRLh2UWyEw9i6TEiPXcRcmwUG13Ilba3Guw62pU3e+1OK8O\nVK3Yf6uqaq7onidcEybkGDqpgGt90Yom6VSR08enuf8Le3ngay/jOA6FXAnbdir1noGQiqJWJ7lY\nvLV62dnr7nKOnknx0jNnOfbyBL39MX7xN29m59V9hMLu55n3FNVaZdU0bMKWF3lfCPPBN19Bwatl\njdS0w+nfmOCdH9yDPyDj88u0dS4c/qUqEp/+9Zt535uq94Zc029373CSSU1kcPYQjmhjHJRp3zfs\nWX9dzBWrapummxWiGve51yuLMtf3XMPvXf/b/MFNv4cUtdDDWc4OJXlFd7+b7sI0OzcmmElrrn14\nDa8ZbKuIKAdc+6jXN7owd4TRQ3/G+NG/ZGb460C1JrUWohIGHCZPfIHs9HPucWqs7hjF7yoHneEC\npZ+A73qu5j3qF2ARv28mw7mc+xs7nMpxOlNAEmBDeI2o/qRCFATiqnLBiOqHdryfPZ27Fjx2eiLL\nU4+epFhw56FsWuPv//dTPP1oazfSqq5NN9FthysTYX5jxwBXrdL26zgOlp5BUhoJrz8ySPuG+wh3\nXA/eRlwgto1o180V2y9UFVUAa24O/6bNCLJcUVQt2wGvhEtOJIjccmvDawWv2o2j65SGRhDlIIY2\nTW72AKOHPsPs2W+5qerr7iYY3068703Ivg5K+dGmnQxMU6foqMwYMt0BlTafwrsGu+kPu2uEhN+P\nhp8vZHQePfcEAH3hqqvs+ak5vnh8DKPG+RFVIyii3NCiZrxQ4scTKeKqzHs3ueeYXoWiP1tMci47\nyhWJLdyx7lYEBPZO7F/x+dZw+eAnkqjaZgHb0vCHN9C15ReJ9d7l2Xn/gUL6OO2Ku1iY0jQQpEoY\nCLjhO9Hu21CDPXQMvoe+Hb/JwJ5P0rfrdwkmGhOKm6E31IXt2JWavMUgeoqqlc1iZTMoHR1Lfq/B\nXdVryjz9VNNjikNDiIEAam9f08cFQXITXBex5NYGKQEV668s2ViCTGmuMS3ZymQQw2EEWSZ05W6E\nziDZI/sWfV+O41CYOwqORaTzBkRJpaRXiaocj4MkzQtT8hTVeYNlNVCpWqea1xqJqqUbJANVq/mT\nj5zk4W8dxjRt9JKJXjIr9amRuDvwC4JQ6Q0KEE+0JoYbt3QgCLD/uXO88OQZwlEfH/jVG/F5GyeB\nkIokCYydSZHLaKRTrlIiK+7POGq7RDcmd3L1lg5mp9z33t5V/5pdvVHe/6s38K4PXYu0hFqzoF9B\nkZsfV8KhKPnwWUUGrXFMMcQr/btAECl5zoRsqVDp31eqUVQT/njD+XpCXezqvIJTVzxLKK5wYsyk\nKIfRzgwT9+zH6fzqdmXXsDrYZtGtbQcEQSTWexe+0HpEKYilpytJmc3KICTFvUfL4XMgNCSlV4hq\n6CdDUU3XEFXzAlh/989mEQUYCPmZLRmMFUqsDwdQm/zWT4zM8blvvFwZ79bw+kWbTyFvWpSsld9j\nZaIa8y2sVk6dz/BvX9nPob1jvPBjtw/9+Igb5PfKS+MLPXXFmPRspd0BlYGwf8W9U8uwLQ3HMZHV\n5oFMobbdtA38NL6gu36oLXEow5iud875N7ruvHKNquk4hK7aDUDbvfc1veby44VXDqH4OzFLSXKz\nL2GbeXzhDXRf8Wt1bWR84fU4tt60k8F3J+EfrHdjITQNQysnJM86IfZOHgCgL+Se+0gqx7fPTnMy\nU2A4W3VJCYJAe6CdmZowpR+dT/Kl42PYDvzsxi56Aj5EVqeoHvRa2V3TeRUxX4TtbVsZzpxjurDy\ncKk1XB647IlqMX2CscOfY/zIX3D+2N8yderL6Is0RTbKgT+eRTXW8wa6tn4IBIHk2W9TOPl5ADKm\ngC/UX7dD1gyCKCGr0SUPjF1e8u90cWlEVfAUVX3cHeCVjs4lPQ+g58O/xvr/9j8IbN1G4dhRzLn6\nCHErl8OYnMA/uKmlSisInoK2iKpaaU1Tbn1Tsf5amKKKnmx8v2Ymg7wuxtz4Y0yc/nt87+tBfMPC\ncfJ6cZLxV/43qZEHAQgm3J3dWkVV9PuRgiGsQpWohjzSWVZLyyhbtvX8GI5j49gmuaKBJAr4a9TQ\ngiFQUsJEPRI6eiZFMKzS2+8qQoWcTmbOHcCjNSpmzAtjMuUSAV/rGrFgSKWnP0axYKD6JN7+3t1E\notXzSJLITXduolgweOgbrzDt9ZgcGHStN22Su3i4dfNWBEEgOe1aMJuppsGwj3jb0ltStIIGFL2e\nbP1nnkLEYjp4LeD2zwWwBZO5rGs5qrX+lhXV+djdsQtbMvFdUcBx4GzXdWhnhol5inI6t0ZUXys4\njuMqqjX99mI9b6B72y/Tt/M3SfTfXfl7s57Sje27nDprsPu8CCVT8qy/r/8a1Tm9Oh6tVlGdLJYY\nL5TYFg3VJZ7e0tW4KQTwtw8c5sCpGb733NlVve4aLn20XYDk37TuzjnxRYjqiVcmMQ0bRZU4evA8\nc8kChYu8wThVQ1QvBMySS7yaKaq1CMSuQBCVupY0ZehT9WvRMlGVPEXVtB3a33Ef/Z/4JPE772p+\n/q3bEHx+ss8/hyy5JV96fhRJidG99ZdQA/W1o2VhpTQv8FOzLI7kqmvZZkS1XMvcEaiKM+vCvbyS\nzPLPQ+crfzuVrg9s6vC3UTSLFIwCo3mNh0dnMRyHt6xrY2sshCwKtPmVSjeNlWAk565/tybcVkE3\ndF8DwIuTzetx1/D6wWVPVAvp41j6HLalYZZm0LLDZCafXvA55jxCBeAPbyDScaNbayoYyBgUnABq\nsLnKuBp0eilp0008/c1QbodiptyBczmKqhyP41+/wU2Pc5xKDHoZ2tkzQHUAbX4BLllbzP5rerHp\n862/smRiiQr6TP37dUwTW8sj3uUjM/k0VmkOxwAhInNuPMVj+0YrSlwZtqkxPfS1SosLX3h9pW2Q\nS1RddUL0+xFDwYr117FtglMjSI7VQHQkOYjsa6NUGGVm+BuMvPxptFKRUECpbD7Ytk3BcD+HTVd0\nEgqrtHeFePeHrqVvvbsIzOd0MmmXhJXJLEDMU1R1X3HRpMSdV/fhDyjc/a4rmxLM3Tf0s/PqXmam\ncjz/I3enemDQnbwSxBAQuLpvGwCz03kkWVywLna1KAF52T1/fOsgV10/ALaXaOgpOIJkMe0R+Hqi\n2nwBcIU3Ec0kzhFvCzAe3Eh2TiMuugusNUX1tYPtpfmWLb/zUVv+0FxRXdyOp5s2M/kA7aEiuv76\nVvrOZot8+2w1xdNYJVHdP+MSiWs6ImyLeao3sKuFk6Oc5H3mfH2PZcN8/W8Q/KShvayWLUM9tx2b\n8dxExU21VEV1znP73PbmLTgOvPjkmUrS/MXCWMEdm3ouUNukfNKtf/SHNy54XKTrFvbc9d+QffW5\nIVYhj53LIUWqG0b+je65yoqq5TiIqkpw2xUtzy8qCu33vgMrm6H40onK32vXr7Xwe61xitn6HuVH\nU3ksqmJKb6Dxc4qr7j2yKV4N6xzOCfzz0ASSIPDhbX3IgsBQpoBeo8yX61S/ePifeXjEXWP+3OYe\n3tRX7SbQ4VcpmDY5Y2XlHDPFJKIg0u53P+c9nbtQRIUXJ/evta57nePSa4S1TJglN2msb9fvIAgy\n549+nuLcUWzrHgRRYfLEFzH1OSQ5jKSEkZRI1aLqq2/JEet9A+CQnX6eIBoF/KiB1atO81GJ816g\n71QtBEFAUFUcLwxpOYpqGWqXWyNgTE3CzmptScnrW+obWN/69T1FeTGiOt/6W25PI8k2pqhiperf\nr5FKIoRkkMEf3YKv+z5GH/tTIutFXtz7feL+GR546m7uu6O6U5lLHsAy0kS6bsUXWocaqMall3QL\n1SkrqgGkUBhjagrHcZj55tdJPfx9ru66ibl8YxBVILqV7PTzFNPHAOgOTHLHhgxm6UqSSZF/+6f9\ndNpdIEA0HuCDv34TkiQiigJBT+kr5HXynnIYilSJalm5NP0ai2Hbrm627uxqqc4LgsDtP7WVdKrI\n2FlXHS8rqkEzyn+/5RN0BNqxbZvUTJ5ERwhxGT0Sl4r3fvg6pqZy7LRsvvHdAls3d3P7L7+bDkvg\n4F5351N2PFIumsykNa6gWqOqCj78sr/puYNKkJgaYaIwxU/dtpHHHjjKmcRuBjKulSndpDXQGl4d\n2Fa5NU3zzY+ytRda1KjWENxw+7X4miwCiyWL6VyQdbEcktNYLvB6gWZZfLVGpQDqar+WC9txODCb\nxS+JbI+HUESRX9zSS0/Q13o88RauQ+NpsgWdSFDl1Giaz/zLAXZsSPCRd+yspKWv4fLGSlrUPD3+\nAl87/q/ct+ke3rrxjaT1DKqo4Jeaj91lpJMFAkGF7bt7OLx/jFNHpyrzJLjOjNVac2vhOA7D2SIR\nRaoQ8tXAMovkkweR1BiB+PYFjxUEAUn2A/WfqznrrncC264gt8/NB5G9bgRyjaK6FCTedg/5Qy9T\nPHAS32bXatyKqMq+BGqwDy0zhGXkKwGgLyfdzajrhENkQ3tYF2pm/fVCH/29CEKQzvANfHN4Cp8k\n8uFt6xgI+1kf9nM6W+S/vzTEL2/rY1ssRLtHVI8mzxANm/QE/WyJ1q+d+4I+js3lGc1rbI8vv354\npjhLwhevbPb7ZT+7O3ayb+ogQ8mzxFh+m8o1XB647BXVMgkVRVf9CrVdjeOYFFJHKOVG3Foox8bU\n02jZ0+STB93QHEGq1EKVIUp+Ev1vI5jYTZAiGj7kQHeLV145FkpJa4VyoBKAvAKiqnR5PbjmWVGq\nRHWg4TlllK2/ziLhT0ZpBkmJVFr5VBVVC1NUMJP1RFUfHYWwe2410M13nx3D8Mb6GwZOsrUzxdDQ\nSyQzLsFzHIf87H4QRKJdtxCM70D2VS1tZeuvIfpIFkSkUAhsm9T3v0fq4e8D0EmRuWyjIle2D5dx\ndd8oOzpHmTj+N5w+vBfLcpj0BsJwxIeiSBU1ohxyVMjpFAvuGwiGqpNlZ08ER3DQw0uLUl9sApck\nkbf97C4S7UHCUR+RmJ9ASCE7p1U2QdLJIpbl0N61ukCJVujojrDzql4Ge6Pk5SDnBq9D9PkIBFV6\nt7kTqOpZrQXJbFBUI4vYqXpC3SS1FP1bY0RDIuejWxC9BMU1RfW1g21WW9O0Qve2XyHRf09dQnoZ\nsve9K4Ee2tbfS6itsa5f003miu4YIrFwa61LBbbjLLu+9PHxJFnD4g09CT62wy0/WI2iejpbJGOY\nXNUWRvE2p3YkwpW6s4Zrth1mvbFVN2x+53NP8fH/8yR//vUDlAyLA6dm+PZTwyu+nouJ2akcD33z\nENn04pt/a3CxEqJ6aMZNj/3O6e9zLHmSdClD1LdwmZNl2WTTGrG2IIIgcNOdbu1mbfhfcjqPXrpw\nQWnTmkHWsBiMBC4IAc7N7MOxDSIdNzaUJiwVZtrdSPb1D6D09BC58ebKtUk1NapLgSCK9PzqRxCK\nVUeWk7YY/39/iV1q3Lh181IcCnNuSn7BtDiZKdAla9wgvcLPb4wiN9nA7vSryILA0ZRGV+wXKLGN\noCzxke39DHiBSzd2xkh4KdL3n54kqRm0eSqnJPeCINIftBocgxu855/JLv83q1s6GT1LZ6CdkZzG\nl0+OM13UuaHHs/+OraX/vp5xWRNVx7Gw9DRSDVkJxr0Gydlhihk3Xa59w88ysOc/07/7v9C787fp\n2vrL9FzxkZaLLV+wl6Cg4SCiic3r6JaDH51P8uC5qr3LL/uJKOElW3+h2qIGQO1Zfu/YsqJajksv\nQx8dQVBVlK7WhHwpNaq2bWDp6Yrt132eiONISJJFXg7hpOv7bJXGRhHC7g5ewfDzxP4xdL3+llyf\nyDDjLUb0/CiGNo0458ecaFSjNY+oHuh7Cw/8YIKCVwM58837K59fVNBJ50sNVhE1uA5Jrd5HHSF3\nQW5bJfran2LblmEcb3IJR+t3IgOhsqJaQisYiKJQaTUDkGgPMnvTQfLr6tWT1cDnV3jPL1/H+37l\negRBIBoPkMu4icOO4/DCk+4Cc7H2M6tFOWwqr1UXHeH1MU5hs26nF2lfY/3NlzQE2SSqLk5UAaa1\naa67eQBHEDk35X7+a0T1tUO592mtcjofvlA/kc4bmj6mBnvp2vKLdG/9UMvnF0sWOd1zYziNvZcv\nRTw+nuRTB4bJLtHWpls2L0xliCoyb17XRsyz3K1GUd0/46rPV7cv7TefzGpYtsOODQneesMAuze3\nEw4o2I7DvbdudI/JXJpE8Pgrk5w5Ocuzjw8tfvAagOUTVdM2OTl3mogSRhJEvnD4K2T1HLFFxu7M\nnIbjQNwrOenfmKiUx5Tx9S/s5bHvHq38PzmdZ+jY1IotnMNZd74ejKzeAec4FrmZFxFElXDHNSs+\nj5n2+kTH4wz+z0/T+9GPVR6rWH+XsTGltHfQ9Z5fwMm5Y0zx4BC5vS9QPNWYohxKXAkI5FNuANHh\nVA7bgSt87hpMkJrbowOyxE1dMdKGiWY53Nmb4ONXrq+rZ93dHuE/7Rnk7v4O8qbFXxw5h2Z3Iosy\nsuSWyj0x8i3+1wt/Xnfu9eEAInA2t/CYXjSL6Fb9PVp2H0Z83fzjyXGOzuW5f3iCjoC7KZ4uZRvO\ns4bXDy5rour24XOQ1aqVU/a1IcpBSoVRtMwpBEHG57WXESUVxdeGP7y+oQC9FmqwlyDujylnri6F\nMWuYPDo2y9OTc3UTREegjaSWwrKXVgskqu5AoXR2IgWXPxiL4TBiIFCnqDqmSWl8HLVv3YLtbsrW\nX9tuPcE1q/t1n6wgyxY5JYSUnaubiPQaovrssTyW7aDK9UEIWzuSZDyrZ27WLZrXnhji3P/6Hw3X\noOsWPtsg4ynls4J7XwiqSt9vfRyAsK1jWk4dsQJXxWwbeDuyd/0h1X28JL6dfMHP1s0jDKybcB+L\n1A/yIS+NtpDXKRZ0/DW1rZVrU4vI0sL1qcuFrEiVROBozI9tO+QyGi+/OMrp4zP0DsTYsWf5mxrL\nQbhJQFWmoJMCetrdjQJJMTlwaobT4xmyhjt5twpSKqPXay4+kZ9i23UbCVk5xu12VMdZC1N6DaEX\n3M0WNbDy+8ofGawLY5qPom6SL7n3lSRc+kTVcRz2TmfQLLshZKQVXknlKNk213VEUUQR1XNnrFRR\n1S2bw6kcCZ9cUS4Ww7SXUL55XZQPvHkrv/vePXzqozfz//7jXdx9o1sKYlqXZu3X7JTrThk6Ns35\n0fRrfDWXB1RJJCxLSyaqw+lz6JbOtd27ec+2+8gbBRycRYOU0kn3N1DOZhAEgVveuAlJEup6cI+f\nc9cDhbzOd/75AI/82xGeeOj4isjqCe93tymy+jyGQuoIlpEl1H71guPUYrA8RVWKNc51FevvMt9r\n5OZbEDQfjmmjHXTbCurzckfAzQLwhQbQ86NYRo5Dnu130HGfI0o+CieOM/Ptb2Hr9fPpnb0JgrLI\njniIt65rJ6I0t/7f2h3n3YPdWI7Dd85luHPDx/GrO3AcA8uabmi/6JNEeoM+RvOllhtytmPzqRc+\nyz8c+Vrd38vuw7S1gbxp0eFXGM2XeCXlrp8149LcUFvDhcFlTVRN3d0dqrV/CoKAGlyHpacxtGl8\nkUFEcXk1C2qwj6jPHaCy81Inp4o6B2db795YjsPXhs5X6gFemslQnuuPpKrWz45AO7ZjkyrNNTtN\n43m99Frf+g1Lfh+1EAQBpasbY3oKW9NIP/kjRj/zJ2BZC9p+wW1wDdUUvGYoJyk3I6qSZFGQQ4i2\nhZWtfnalsVFELx133ymNwd4o8XC9VTURLFEszmJbGoXUYQRTxR4t4pgmjlX9bvafnGZoPENIrA6A\nKSmBIMv0/NqvE9y5CySJgBcG06zOMRDdTLz3TZX/247I0SMCLx10VfpoNI/oWPj89QN3MFS1/mpF\no24yLsO0TZRF0qNXg5517mT4zA+HeO6J0wRCCj91386LUp9aC1WRUGWxjqiWiWRnNIoqqbS1O2i6\nxWf+ZT/n5tz7pC3QPIW0jJ6gq6iez08iigLbwykcQWRQsEnn12pUXytUiGrw4myA2I5DtqBXFFVV\nvPS/67F8ibSnpA5ll0ZU90675Oq6Ds8Kvcyatfk4OpdHtx2ubo8iLtH6OOO5HDpjjYt7SVq+4vNq\nwXEcZiZzlf7Uz/zw1FqYygIoGAV+eO7HmLZJm19hrmRgLeHzOpZylbrtia3c3ncTN/deDyy+yVhu\nm1ZOuwe3Jdovf/w2bryzGtqolyzSqSJPfO84xYKBP6hw7OUJJsaWV5eeM0yOp/P0Bn10rjLx13Hc\njBKASOeNqzqX6fWIl2ONc91yrb9lCIJA0NqJ/q/jWLPu+XMH9jP+//6yIWG4XFs7nTzJUKZIr6IR\n1EcJtV9Dbt9+Rv/kj0g+8G2yzz1b97ywIvOJ3YP8/JbeBW3UgiBwXUeU39q5np6Ayt6ZLA4ipjUB\nuOuw+WR1fdiP5ThMFJpvNie1FEktxen0mbq/z2hJBCHElBag06/y0e39BGWJJyfyiEKEorlGVF/P\nuCyJaj55iNToI5geyatVVMG1npUR6bhu2ecXRJm+XneQmp9Q9vDoDP9yeoJ0izTKyaLOy8kcz0+l\nsR2HF6bSKKIbWXF4HlGFpSf/Wp6NxNe/MKlcCGpXF45hMPQff5fJf/gixVMnUdf1E7vtjgWfV67l\nNbTWbX/KQUryvIAqQVSRJYuiF75iJt33axsG+sQEUodbx5bRfLzvjZtRlcYdTLs0RT55CMcxESZU\ninIYB4GpQ0cxTIt9x6f5y2+9giyJrI9JSJ7yO1VQ2fTZvyBy7XVu4EEkgs9wJ9G5FvbRWju45fgY\nO5smEndJUzBQxE+pYfCWZBGfXyab1tBLFv4WRFW+iER125XdKKrE8IkZHMfhrfftqii9FxuhgEK+\nlqh6n2087KPNn0Ajx6+/cxcl3SZZdH+znYsQ1V7P+juSdXeLN21tI6iniToyxcylT15ej3AcB6N4\n3nWtrEJpWAhfeeQEf/OdI+R19zekSpf+AuTwXHVcP50pLkqaZjSdMzmNosVCPwAAIABJREFUTZFA\npWWIJLhzhL5C6++4l3i6Lbp0t8102iOq8SZEVSwT1Uunj61pWOSzpcqGYP+GBJu3dzI1nuXU0YVb\n0v0k44WJ/Xzz1HfZO3mAdp+CDaSXUB96LHkSURDZmtiMIAh8YNvP8jObf5o7+29d8HnlXuK1yfcA\nqk9umJOefOQkZ4dm6d+Y4M33usTq5JHG/p8L4cBsFtupbvqsBqX8OfTCOIHYFSi+tlWdy5wrE9XW\niupKNoL8fYM409X1S/H4MXJ7X2D885+rq1cNxtwk4UPT0zjAoHUYxd9Fov9u8i9Xazqz+15seA1V\nEpe84dUVUPmNnQP89EAH17YH0ErV8815KdFjufM8e34vnZ4Dq1U/1fN597vP6Nk68jmem0BVtuEg\ncHtPnLAi8471nZiOQ8D/Bgr6pT9PrGHluCyJ6uzZb5Gdfo586hBQVfzKKDdgBvA36W21FIQVd7c2\nM09RLf/ApltEvE8W3IHifKHEyXSBlG6ypy3ChkiAczmt0keqs5L8u7xmxWpP7+IHtUBZjRUDftre\ncR+Df/wZNv6P/0lgy8KfkeL3gpiK0y2PaWX9FUQVSbawVHcxZMy6x5kz02DbEFYwLJH+7nauWJ9A\nalI7IVhJcrP7AYHUYZ1nNr6H4x038oOvPszv/t+n+at/ewVZFvn379uDqJewPAW9kNfJFasLLTkS\nQdZcxaNVcqwkVRd6huGeZ+uuAWxHJRjQCNB8gA2G1coEHQg27uqajoW8SGua1UD1yezY7d4bN925\nqaEm6GIiHFAaFFVVFvGrEm3+OEWzyFVbY3zsvl2IPvdzbw82Ji/XnVMN0R/u49TcaTRTI7hpE9tm\nXgBBoKNoYq2iaf0aVgZLT7vtuwIrH4MWw+P73Y2JsvXXL136mxKHUzkUUWBbLMicbpJahATsm3YX\nb9d3VhfWgiCgiMKKrb9J7zXLxHcpODfpEuye9kZyW+nzeAlZf59+7BRf/ZvnOXfadfa0d4e5+a5N\niJLA80+cxlxrqdMU5QX/mcxIJVhrMftvwShyNjPCxuh6Al46uyIp/NSGuyoJr61QDknyBxrvxVC4\nfm4cPZPC55d549u3078xQSCoMHR0esnju+M47J3JIAkQndW4/wt7KbZQ6xY/l0Vq1A1djHYtTMab\nQZ84z9jnP4c+4bpOrPQcCAJSpJFAlxXVpSjb8+Hrb96hQR8bZfLLX8LMZhj+5CfIPPEcvvAGjhVD\ngMNmZwThsErmyacwZ2dBEPANDFA4dhQjlWp6zqVCEUVu70nwnk39fGDbXWxPuGvKlJZCMzU+f+Dv\n+aejX+cHZx4A4Hyh3nli2iaO43A+V92kmCq4682R7BjPT+zDr7j1rzvirrixuy3M9ngIWe4jY6wl\n/r6ecVkS1TJKubNN03t9kY1Eu2+j54qPrjgBruzLrw3HsByHlDfAt2pcXG46rVk2j465pOzGrhg3\nd8VwgKcm3LqM5SqqSrerMPm8PlwrQfwtP8X63/9vbPr0n9Fx38+itC1tx1BSYwiiiqG1JqpGaQZB\nVBqaY0uSiiQ64KUWG2VFVfOSfFWLjKYy0OU+r1apEf3uhkOHegajOEEgupXpWfeWHYvvoK8wQbFk\noioi/+F9e9g2EKeQr5+A8zUJv1I4imiUkByLuRZ1jrWKqlaUkGWRzds7QYgSCGgExObPq90pbmX9\nlYSL2+bhxjsHeecH93D1TStX3VeCcEBB0y1Mb3GRzpeIhlS37tez5ae0Oa7f3sXVO9xJJuFfnEhf\n1bED07E4ljyJb8NG2ovjhI0kYQTSK0gOXMPqoBcvru23VoksWRKGJRKQL+165KmizoxmsC0WZEPY\nHTtmSq2v2XIcXprN4JdEdiXqyxxkUVwxUU2VDBRRICwvbTPMtGyOn5ujtz1IvInzQhAEJFF4TRTV\nidE0hSaOl5HTSUzDZt8zZwHo6AoRjQfYfX0/2UyJQ3vH0IoG939xLy/8+NJMK34toNvuZ3k2c67a\nS3URonpibggHh+1ty9/o13V3zaT6qvei7Tg8OZHC8DXen3fevY1wxIcoimzZ0YVWNBg6OsXomdSi\n7oSxfImpos6OeJgnv3OMmakch/Y11mwuBfnZgxjFSUJtV+MLL38OzR08QH7/S4z/xeewtSJmJo0U\njiA0yaZYbnuauucmEoih+kR1/5at+Ac3kX3uWcY+++cY01OkHn4IyX8NE3TQzQzK42fJfP/HTH35\nHyiNnEOOJ4jcfCtYFsP/5fcY/eyfk376ybqSqpXgtr6buKbrKgCS2hwPDv+AtJ4hIPuZzLvhZ6O5\nagnYZGGaTzz533l89CnG87VEdYakluJvD30Z27EJq72EZImwtzYXBIF3rnfX/kW7b1XXvIZLG5cd\nUZ0f6BPvvauhDYIgiMT73ryqBVXEU1SninpNs2uzUm/aqmn2ZA2BHSuUWBf00R/ysysRJq7KvDCd\n5tMHh5kz3Gteai/VgU98koH/+geona1DoBaDqKj4BzchyMsjTIIgoPg7MUozTXupmvochjaD7Gtv\n2Bio9FL1dvq1KZfs2qUSiCAoNprmI+iFVtUSVX+wi5IhElbcnX+fOEhRrO7+RyyTKwci/MEvXc/W\n/ji2oaOZ7uvLintrl9vFAJXG20FL4/xM87YXtf0htaLM4BUdqD4ZxRdHkhzags1r0No6q/fgfOuv\n7djYjn1RFVUARZFYtyFxQfvTLQWhcvJv0cB2HDJ5o7L4LcfWJzV3x9aU3M99sTongKs63Kbjh2aO\nIgUCqN09hDX395Jr0mJoDUvHSmr6qkFKF0dRrVXlQaCgK4RUo7IBcimiXM6xMx4mWnbh6K0V1RPp\nPFnD4ur2SKWFTBmqKKwo9ddxHGZLBglfY4hbK5wez1AyLHZsaO1skCXxVVNUzWyGsf/7WWYPHeNb\n/7Sf+7+wt+7xfK5E1rP8Z9Maqirif/lJzGyGa29Zjz8g89wTp3nw6y8zM5mrkNnlwHEcnnjoOEcP\nXrh09ksBuuWOlaO580QV9/6YbbHRDpDT8zw0/CgAO9q2Lf/1Su4aoVxDDHAkleehkRmeSWX56fdc\nxc/9+k1cfdMA19+2gc3bq2uaXde6m9OPffcYD3ztIC+/OLrga+3zkq6v64gS8dL4ZyaX1gau4bqL\nbljiSmtT7aJrpdcnzjPxpS9izqWR483nOXmFNargrsfKJWCK16owcv0N9P7GbyGFI5TOngHAymQ4\n8uBzOIisN/MMfOA/E7vzje61ahpyWxuJt7yVjve+H9+6fgqvvMzkF/+e5IMPLPua5qO8EX149hhP\njD5Nu7+NP7zlk9zZdx2Oo5MsVdeRPx59Bt0WeWnyEOfzE5W/n0id4v/s/xtmtST3bLybvCnQPa8G\nOe5TEJwUNu0U1xwVr1tcdkTVqrRHiBLtupVI1y0X5XVCskR/yMdwtsgD56a9xUB1cG+lqE4W661q\nN3Z5yaeCwM9u7GJ7LETRtPnXM3P45J4lW3/lWJzAps0rfDerh+LvBMemmD5R93fHsZg69U/gWITb\nG6PcJcWdPFTVI47Tbi2rXSqB353IBF1lbtglIKJSQxSPnECZLXjnieLMQL7G5p0K93Nnl0Vvu0sS\nrWwWXXaJbLl/qFasUVQ9otoXcHj6lQkOnpppuF5BlDBtl8jrhsLWHa6SHfBqKtfHm1tkOrqr6sh8\nRdX0kp0vZo3qa4lK8q9mkvPIaswLmCpPWKnSHI+PPMWx1Em6gh0VO9lCGIisI6pGeGX2KLZj4xsc\nRDW9NjdrLWqWBcdxGD6f4TtPD/O/vryXj/7pEzx3eGLxJ9agTFSV4OqJ6ky6yANPD3NipBomNzVX\nn/CrWT5Cqk5pAeL3WsJyHF7ybIfb4yGiqvv7zizQomZv2fbbpJ5OXqH1t2jZlCy70n5kMRw9k+Qz\n/3IAgJ0bW7tqJFHAepWI6vQjP+DAqMTRp92WJYW8TqkmmX1qvD5gZ0soTfb732H0Tz+NpOW54XY3\npGfqvKvUzG8hthQU8jpHD57niYeOr/RtXJIoE1XbsTEsd70xUWxtqX9w+BFGc+Pc1ncTg9HmNtOF\nYOgmsiLWBfkd8+q4h7NFNmxpJ5YIcMsbN3PDHYN1z020B1m/uXpPvvTsuZb9Vg3b5mAyS0SR2BIL\nEvTS+KcnsivaiHO7SICsrqwlYZmoym1t5Pa+gFPSkJoEKcHqrL8AsdvuILB9BwP/5ffp/tWPkLrh\n/2fvvePcuM87//dUdCywvZBcdlDsRYWiJFvFkiVZseKeuMR2nMRO7uL8kjjntMsriXO/OHe5tIuc\n5jh24nNcZMu2qmVJtiOJoiix12XfXoFFBwbT7o8ZlN3FkrvLJir8vF58cReLAQaYme88n+fzPJ/n\nNk6JHtI//8sMLlmJb6tjfNXvVoitj92MZ/FifKuriQelqRlBkmh8+wN0/8EfsfRPPg+SRPbA/gXt\nUy2ibiXVnrEDWLbF+1c/jF/xsapxOaaVImuI7oiuBHsmWwgHP8JgIcRAdoiQ4sRSO4dfY6IQ54Gl\n97ClbQc2Tj/sdHiECQRBrDg/X8ebD9ccUS3P8Qs2bSHS9bYFD2O+EARB4KOrumj3qewaS/Fk/wQT\nNSrqRB1FtWRaTGpGJWDwSCIbG6vzBlc1BPi51Z38zIp2bCDoXc9EIX5NOBYGGteDIDJx9lskh57H\ntp3Mv16MY2gJ/JG1dWcnSrJz85AVGwOx0qNqaxqCXDYUEClmSuRzJSS1SlS1031YCecmG2jazNHd\nR8nWENWc0kA4Xs24mukMmquINrkKZz1F9f23OEr7D1/vr/tZ87pLVEsy4ahLqArOvoqB+opFS1v1\nOE/vUS07373Ziepff+sAX33WSWSE3V6ksqL644GdPHry+zSoIT618eNzel1REFnftIasnqM33Y93\n2XIU0wmwrhRRNU2LQ3sGeH3nuSvyfnOFbduMJQtYcyQ2Lx0c5nNfeZ3vvniWM0NpTMuu9IPO9f1K\nhWEkNYI0y/zpuSJf1Pn9L77KYy+e5ZHHDpFz19Ix1y301nVtPLB9CYLkR5ZsNK1IqTDK8f2PUSpe\nXC/VpcSBeIa4prOtuQGfLFXaRdKl+pl93bI4kcrR7lPpDMxM1KiiOKuialgWL49M0pOcWQlSbkeJ\nqhcmqkfPJfiLbx5ANyx8Hok15+lll6QrU/pr6SX27k/QF93A4WR1f3pPV5O4oy5RXZw8wpJFfjqH\n9wBQGhqi/3/9Kau6vdx022I2ewcIqSbFGnU+kypWtj8fUok3/iikhaBUU4U2nBsg6pEZzpdmjTsm\n3KqVd698aEHVOSXNnDJH3LRtjqec8zah6aQuUHEwtjbK2hu72HTTIooFnYOv11dVj07mKJoWW5rC\nSIKA5h7zfLZEfKx+xdT5YOhpBFFBWKBRnFVwiFLHL/4yUthJRMnhWRTVi3T5Du+4jcWf+SxyJMKu\nxTG+eGKIfzs5zLc0hR8+8AFeeNu7aP6ZDzEeW4+ETXeDE5+oHdUSWXla65fa3o532XK0vl7MwsVd\nC7WtPRub17G+2Zmc0KCGsKwkNgKfP3CW7/bGEUVHFZYkx9/k5vatKK7PyH3dd/GOZfcx6goO0xVV\ngKDkJDtfGIrPMD+9jjcHrjmiaurODUdaYNZrPggoEj8f66LVq7JzNMnjfU7ZqioKTGr6jEVmX9zZ\nt7WRAFubQrx9URMeaeZXHIsECMoSlrAIzTTI6vNfVK80vKHltK3+eWQ1Snr0ZcZOfgXTyFdG1qj+\n+j0CgujOfxVNMkoAO+UEmpZWBJeoWqbzHQ31JRFry7g1C7Mny/CYlxPxJYyd7ENTgmiCS5IlD9Lp\n45WnG+lUVVFtcRXVOkS1STZojfrorZN5TaSL5DRH6S2VVHx+ldzhQyS++X3nCbPE6JGm6h+ml/4a\n9pubqN6+sYNtsRZyRZ3Xjzvum42hcumvc8MayY0SUoN8essnafO3zPpa07G+pvzXu3QZiuUQ1doE\nxOXCyGCKb395Dy/98BRPffsQun71S4sKmsHzewb4vX96ld/++1d46dDcyhTPDjtr04fvW83f/Nod\nrFkS4eRAikR6br2+pp7BMvIXNT+1jOFEnpJuEfDKZPI6//j9o/SNZqpEdX0777tzJYbtXFPZkScY\nPvaP5MZ2cvjIf1z0+18KmJbNC0MJJEHgzg4nGVNWVDOzBEtDOQ3ThuWzOPMqooBh2TPWJMu2+eee\nQZ7sn+Crp4ZnlBaXjXEaPedfX/rHsjzy2CEEAf7ruzfwZ5/agf885kuyJF4RRbX3+V30+Z1qIZsq\nMTp3slrxMtibBNtieXw/608/jj1wDv/adTQ++BD66ChDf/55lsb303T4OZTJEQzdqlyvz373CN/7\n2n6MC1y/5bEqbzaUzOpaeS7dR4fPQ84wZ4zeK6OgFxAFEY+0sFEvJc1ArSn77csWyRtWZVbwWXeE\nk2FZ5HQTyz3fDcviyyeGOJkv0rKtgxtvX4rXp3Bgd/+UxEMZtWW/AMVC9bp4+fn5jywySykkpWHB\nrTNlcqd2dNDxyV9BUFW8y5bVfa4oCIgsrPS3FsN5jReHJ4mqMg8sbuaBxc10B730pPI81b2OMW+Q\n7pAf1Y1D1bZ2cD+f0tQ84/X8q2Ng2xRPnZzyuKWXKJw5Teb13eSPHb1gH6tP9hJQ/CiiwntXvbPy\neFgNYZrOdS0KoHKaXO5R93dnXby3+04eXvEA7175EO9cfj+CINQQ1ZmVEkHFQCsdYryo81TfzCq5\n67j2cc1FzmVFdaHlGfNFUJH5xJouvnh8sOL4u6rBz5HJHMN5jcXB8rxVgx8MxPFIjvtZOWipB0kQ\n2NQU4uVRE1lezHghTtFUebp/gptbw6yJBGfd9mrC4++kfc0vEu97nELyGNmJvRVFW57Fyl10XXwl\nUSctB4jmR7B0HUsrgVRVVAGG+pN0LvVhF20EScAumthDRX78YjOnwr38rDubSwp5sdIaWSWEcfYU\n2uAAnq5FmJl0RVFtrKeoBh2imnjicVav+ylemjSIp4o014xnODmQIu+OxijpCh6vzMDTT2IXnIXZ\nluoHobVlTp5pAWNFUb3MZkpXC60RH//lXRswTIszQ2l6RzPcus4hNA1qGK/kQRZlPr35l2gPzK/H\nek3jKmRR5nD8GA9tuRvFNQZZqLPjXPHai2d5/WWnz80fUMnnSqSThUoC5Grg2LkE/+c7hyjWKHYj\nibmVO426Qfht6zvwqBI3rWnleF+S13vGue+mCxuHVI2ULr7sN55yyPE7bl3Ka8fHOHQmzqEzcTxu\nj2erez1agntdFk6R0VTC3hLFwsL6zy419sXTJDSd7a0NRNwKGp8kIgvCrD2q/Tnncy+uo6aCQ1Qt\nwLQrOTwAcoZJb9bZ1rRtfjgY5z3L2ip/rxDVOqTTsm3+9tuHiIY87Ds5TkEz+eQ717F19YWTRVfC\nTMk0LV7ZOwliAz4zR0EKoKgStm2TdNs+0skCY8MZGgsjyLaO1u9UwvjXrSd63/0IikL8e4+ReMLp\nrVPdWdnFvE7O1CrlwJl0kWhToM5eOEjO8Vq61lAu/fVKXs6l+7ljiYejyRwjBa1unJI3ivhl34IJ\nW6lkEmqonuPH3D7uO9qjPD+U4FymyKbGEH97tJ+xQglRgKAsTenZ7ssWWRsNsvXWJex84TT7dvVx\n613V1qekpnM6nWdJ0EuLT8W2bbSiTvuiMB6vQu+pOE8/epi33r+agJs0tW171s9kWTqWWai7vtmW\nxeBf/wW+VatpeuiddbZ2X8MlqqLXiz+2hhV//QiiMnsiyLm+ZieqY4USPakcy0I+Fs2yZhxMZLCA\nBxa3sL7RuTdtagzx98f6OZrMoYoCP9VdvdZFjweluRl9fBy5aaZTri+2Bp56gnzPcfzrNzD59JNk\nXtuNNjQINeRUaW9nyWd/r5L8r4ePr/sgkiDR5Kv2wYfVECX9KF0BDz+9bDt/ufcFNjWvY8wUKRLm\nwdUPE1KD3LX49imvNZgrIgDtdRRVr+ShqO0i7N3IYP6N7xJ/HfPHtaeolntUrxBRBccB+FfWLube\nribu62qqZPD2xqvlRE/1TVA0Le7rajovSS1ji/saqryK/kyCvzvWz/FUjm+eGV2QocaVgih5iXa+\nDXDmqpYV1VmJqugsLCIOUQUwJiexS9oURVWWRUdR9Xig5Hx+W3P+95g6kmkguK/V0OjHAIpuL8Pk\nM09j2zZmOk1pBlGtEho54qh7pZFhbtr7XSTLpHe06j4HcGogRcEt/RVEL1pfL4We49hu879lzb4Q\n3n7vSlo7Q0QapyomZaKqXGYzpasNWRJZvTjCvTcurpQDS6LEb2z7FX7n5v+PzuD81Lhc4hBCaZLV\n0RUMZoeZNHLguh1ql5Go2rbNnp29+AMqD39oMxtvduYyp6+i4mLbNl9/4RSabvKuO5bx2Q86/eCF\nOcxDBBhPFmgIqnhcpWP1Eid4GI7PrZqjYqR0KYiqq+K2RX383ke28Wvv3cj6ZY1ouonfI9MYdpN/\n1hIGkkGeOLqCF4ecWc+mefXdng1XTZUFgTs7quueIAiEVXnWHtX+7IWIqnM7nr7+l0slb21toN2n\nsncizVBNQFYmse111IaReJ79pyb40b5BktkS779rJbesbZvxvHqQXIX3cuLAs/tJiQ0sUpKs8Dsl\nfI2NXkINXjLueVKekdqWOUNg8xYCGzch+v0E3fnYTT/1MM3veT8A4dvuwCM5a3UhX+Lk0ep81fL4\nsNlQq6i+mcZflawSoiCyrGEJE4U4EdU5psOzBPV5I49fWVh5v2lamIZVMVKybZtjLmG6vT2KLAgM\n5IrENZ2xQomIKrPI70UWRdK6UalA6806x2Ld1k4CIQ+H9wySy1T3d288g01VTS1pBrYNXq/CnQ/E\n6FwSofd0nK9/cTfHDgxz7uQEX/27Xbz47AkGzk0yPjL1vl/rfTIdRiJO/shh4t/9DuldO2f97FY+\nj6CqFbPK85FUcAyVZlNUJ4ol/vZIH0/3T/AvPYOV8v7pKJt6LglW15SwKvPp9Uu4p7ORj6zqnKFC\nlst/lXpEdeUqBFkmf+QwE49+g4nvPEppZBjv0mVE7n4bLT/7IYJbt6GPjJB+ddd5P98NjatZHZ3q\nq6JICj7ZQ1Ef4JWhVwG4o+tWwoqMIoa5c9FtM17HtG36c0VafSreOq7mXre9rEEVSJzHcf06rl1c\ncxJPRVGts6BcTngkkbs6naDEtG1CisT+iQzjhRITxRJp3WRRwMMtrXMj0B0+lYhiM2kv4XgqRdG9\nMRZNi1dGk7yl4+KGTV9OSGoEQZDRixMVl17ZU989UnAVVVHQSZWJaiKOVSwiuDclA4n2RQ0MnJtE\ns0TskoXgkzBKEhlvC16rxEe2NZB83vlu2zrCjJ+bRBFl1M5O0q+8jNzYiFUqYUgKogger4yiSlNK\nhrzLV9D6kY+RP3II9u5hBwfpHV3OtlhV5UtkioiCu7CLISaf/QEA7R/+BEmeQ100e6C+YdsiNmxb\nNOPxN7uZ0oXQFZw/uSkVxoj3PgaCxIbmuzka7+Fw/BhyyElOlLKXjzTqJRPbhpb2IJ2LI5Xy8atZ\nGnj4bIL+sSw339DKT922rDIHOF+8MFHVDYt4usiqruraFKpxap4LLqXjb1lRbWrwIooCm1Y2s2ll\nM2OTeWzbSXgAeINdfPGZzdy9tYsP3LWE4cPPw3kSRVcKeyfSJEsGt7VFZiQlw4pEb1bHtO2KYUoZ\n/bkiAVkiOkuJbrlvTbfsKR0GZYU2oio8sDjAv5wY4qm+cT4R68K0bc6k8zR7lcqMzFqcqenNfO+d\nK3j7zXMfuyFLIuYczq+FIp/V2HNgEtk02fGOGJP7DnF40KI5LJIxFSYn8mhFg1PHxhAFaMn1EVj3\nASJ33TNDHWt84EGCW7agNLfg+R//DEAhp3PyaHXcRSZ1fqJaq6iWNKPuPOxrESVTRxVVloaXcCxx\nAsOcAKS6RNW2bQp6oeItMF/obrVHKqLyx3tP895lbcQ1nXXRIB5JpMPvYTBfrBjfvKUjyvbWSOW9\nAR452s9gTsOwLGRZ4sbbuvnJMyfY80ovb7lvNUXT5LXxFIoosKGxbJrorGMeVcSrCrzzZzdxdP8w\nr/zo9BRzrMN7hzi8dwjVI/HBT95SOcbnM1IyklXDt+SPXiC8vf6MVatYRPTVL+uvB/k8iaBdYykM\n2+aGSIBjyRz/dHyA9yxrY8W0toGEpiMLAkFlKoHzShL3dNWfLRq9/0HUjo4p/apliB4PvtUx8keP\noPX3obS3s/i//S5yuBpvh268mez+fWR2v0L0bffO+fOWEVZDJIqTTBTiNPuaiDWuZNfEMCOFEppp\nzWiXG8lr6JY9hYxP/azO4yHFZuzq5zGv4zLgmlJUS4VRtGwfsqcZ4SoG/ZIgcHNLA5plcSZTQBQE\nYg1+3r+8HXGO5TKCILCh0Y8gSPTnncXxF9cswi+L/HAwTm/mjdsvIwgCsrcFoziBocWRlDCiWD97\nWC79lWWTvMdZ7PR4HKukYZTNPySFTtfUYzRugKuk9nlj7Ol6kDvXd7AhqFccf5cvjWAAgg0dv/YZ\nlJZWEk8+TnrnS5iCgqJICIKAz69USIZpWpw7OUHg1ttp/8QvIfj8rM+c4dzwVJONgmbw8tlF7Hpt\nA7YVIPP6btTOLoJbtwMCLCB2ebObKV0OFFJu77Ftsr55DQCHJ44h+50QXr+MJT6lUnm8gnO8GqLO\ne15NonrQNZa5x02E+FyyMxdFdSJVcIh3tEp//F5n++wciapeGEFSwjNGgS0EtUS1Fq1RP2011Qh3\nbOrk85/czofviyHLXixbQESr9LRdapweTPHNF06ddxzOpKbz/FAcRRR4S8fMYD6kythAdlr/X1LT\nSZYMlgS9s5YfqhdQVMOqzKqGALEGP2cyBY6ncpzLFClZNrGG+sel3Jv83z96Iw9u755XOeflLv09\n+PIpDCRW2+doXL+Gps4IN/c/zvpOg4DPCbwHziWIj+Vo8xVRrBJGKTjZAAAgAElEQVRql3P+1/sc\nansHgixTroDuO5MglShUylDPp6jatj2lYkK7jAT9SqNklvBICkvDTpJiIt+PKgqM5EucSOUYqVlL\ndUvHsE38CzRMKzv0no7KFE2Lr55yElw3RJzzsyvgwbLh5VHHq2J5qHq9C4KAIAgsCXoxbLtSKRDb\n0E446uPAiXHiiTyPnR0j5SaKvO6M0nJ/auHlH3H6V38ZfXSEdVs6+cAnbmL91i7Wb+3ivp9ei8cr\nIysiJc3k9ZeqI4wMvVypV0dRrSGqZjYz4+9lWIU8km/u35skCHVdf0umxd6JNCFF4oMrOrins5F0\nyeDLJwY5OlltfbBtm4Sm0+hR5hx3gtOH2vK+n0EQ64f/gQ0bKz+3vOf9U0gqgNzQgP+GtRTPnKE0\nPjZ98wuiQQ2hmSV0y+COru2Igjilv18zrSkEvs89D5YE63+3XjfGzGhvrrFS11HFnIhqLBZTY7HY\n78VisX+NxWLhWCz2B7FY7IqmG23bItH3OGARXXTflXzrurirs5Hf2NDNH25dwX/btIyPru6i2Tu/\nr2R7awu2bWMh4ZNEuoNefmZFB5YNXzs90zTjjQTF24xtG5h6ZlY1FUAUy0TVwAo5RNNIxLE0DUN1\nAghBUitEdWS04PSm2jZZIQKCgGXYaIMDZNUIPq/IyiVROt3xM5YvyKLf+ixKcwtWLochqqhuEO71\nKxTyOpZl8fzjx3jmO0c4emAY0eMhuHEjDUaOYt9U59+CZiLYHuKJKFI6DqZJ9L63I4oiouzDMosc\nmjjKWH7uTftls6zygnodF0Y+WTXJiqohuoIdnJg8hRRyzhl9ljnGlwK6e92Vy9fCbwCiWiaUTW5Z\nrCKLyJJA/gJE9eDpCf74K85MytaoExTalkFu/CVaQga5CwTkpwZTfOnx1zH1zEXNpa5FPF3Eq0r4\nL2D+IwpCZZ8FQcCwFbyyQSpbv7wrW9AZGF94D+sjjx3imd19vHhgaMrjhmlxoj9JrqjzLycGyegm\n93Y1VVx+a9FQcf6d+r2eTDsK0spZjJTA6VEFZoyoKZcSl4O5+xc3IwJP909waNIJnFc31H/dM0Np\nZElkcev8e6slSbgsc1TL/YTnekYRbJN1t612ZnU3txAqTZL8zjcp/eQZAPbtctbn9oLzv6drZsXK\ndHjcUWg9rtHYlu3OiJVMavbrN58rTTGOmm0kyrWIkqWjSCrdLlHtzfQRlA3GiyW+fGKIvznSx7hr\nVpN3x38tnKg6CZqAXSVOgm2zxiWq5bL3Sc0gpEi01OmrLk9K2DmaZDBX5NmhOOe2NTF8SyvfPDrI\nocksHX7PFMWwrKgqpoZtGGT37wMg1ODljvtWccd9q1ixppUPfeoWPvarO2iI+jiyb5BJd556WVE9\n8Hpyhv/BVKI6+/piFQqI/rl/b7MpqvvjGYqmxU0tDUiiwD1dTfx8rAtJEPja6WEOJ5x9KJgWRdOq\n25t+MQhs2ASA0tpGYNPm+s9ZvwGgMrN1Pgh7nOMrizLbO5wxOiFXER4tlPiLQ+f4+mnn2rVtm+Ou\n0/mSWVomyqW/51JH5r0v13FtYK6K6iNAANgKGMBK4J8v107VQ2Z8N6X8EP7oBnzhlVfyretCFASa\nvWrFTW0hiHo9iLaTkeoO+RAFgZVhPw8sbiajm/zfU8MYb9B+VcXbXPPz7MYcQo2iKrlEtRSPY2ul\nClEVVYXWjhCyLDI8nMPYPYn+3Dia4ijNpWKJ7MAwmhKksSWIIAgs7nCyfFrRQGlsYtFvfRa5qQlT\nUivW+D6fimXZPPvdo5w+7jg2pyed7Fxw81YA2sZPV8ZjABRKBn530RRG+5FCYUK3bHf2U/JiGnn+\n4eBX+N7pp+f8XfWmnSBrSfjCQdZ1gJbrRy9UZ3zqWoINzWsxbJO0X0OydAz98l0X5fK1snOlokiE\nGrxXtUe1TFTLvb+CIODzyBcs/X3sP86iuZ+nbFKUSxwkNfxjtncPTzn36+En+wYZHT0HXJqyX9u2\nmUgVaWqYXVmcdVvBg1cxmJiFcHztuRN87iuvk19gEqMcM+52eyJTWY3vv3SW3/q7nXz+/+7l+6/1\nMVHUuSES4Pb2+sm58py/F4biU5TfcqnjqlkIJZyHqLqkt8Elqm0+Dze1NjBR1HltPE1ElVkWmvm6\n6XyJgfEs3W3BSjn1fCBdJtffPTt7+dJfvcxkXiRSGCWwuAsAtc3pnTUScbxucm98JIMkCYRP78a7\nYiVS4MKKvs/vfE+6bqF6ZGIb2pBk8bylv1ph6nX0ZlJUNbOEKiqE1CBN3kaOxU8wnO2h9sg+crSP\np/rGGcs737tfmXsJay1K5QRNzenWkYrjd3sLu2rIxk0t9R12u4NeFge8HEvmeORoPy+OJDFFAWyb\nIcmuPKe2tL5CVC3nGOePHa27fx6vgpY5wo47nPaOV350GgC96CSeTx4vsndn35RtjOQkKW8LGV8z\nVj6PXScms0olbMOYX+lvHUXVtm12jSURgZtbqmXIy8N+Pra6C1kQ+PrpYQ4lMiSKZbfvS0tU1fZ2\n2j7+C3T80qdmVV2VFqddSp+Yv8tuWHWI6rbWTQTdCp1y0u/JvnEyusnJdB7TcuZUn0znWRry0TwL\nIfe4MaZppea9L9dxbWCud69tPT09vwvoPT09eeCjwJbLt1tTYWiTpIZ/hCj7iS56+5V62yuCBtkp\n6ZPsCXK6E8zc1hZhc2OI/lyR7/eOvyHnrNaS01Dr9lmfV1v6q7gzxQpj41haEUNxgjpF9SJJIm1d\nYRITebRxsE5k0SRn0S9qJpNjTsaz0Z1X6nFV08oNqqmZ7j/8EwxRQXUDOp87JubsiYnKXNWc29vn\nX78BWxBYmh9mcLxqKFPQDAKK62RczBK5+x5Edz9FyYtlatjYxN1Zc7WIFyZ5eehV+jNTFZmzaefG\nt3QBw9OvFmzbIjX8E3Rt5ue8rO9rGcR7vweAr8EZTm5oCdY3OXPYhtU0iqld1rEZZVVAqRmxEG3y\nk0lrmMbVSRxlCzqqLKLW9CL5PfIFS39VpbrEL3JVtXzSCeKaA8ULlv4OxfO0hpx1SbkEo2nymkGx\nZFaU4flAlLx4ZZMJl3BYtj2FDPaPZtENi5EFzsP0uce7pz/JX3xjP5/5wk6++9JZCi5pGc/NPsuv\njC3NYVaF/fSk8jzT7wRxmmlxOp0n6pFpOk9QeSEzpXDNsb+ns7HSy3V3Z2Olv7UMy7b5h+8dwbRs\nblozP6ftMmRRmPEdXwqccZOGAE35QaSgc16qHZ10/dpvsOQP/ohQU1UB7giUkC2d8G23z3itevDV\nHJ8Va1qQZTfRdJ7SX80N+svX/JtJUdXNUmXUzNLwYmxsLKs6o1YrHcEjibw0muQrp3IE/e/jZG4V\npQUYSunu2lmsOR27ju2vqJItXoX7FzXxgeXtvG2WHkpBELi3q4mwIrGhMciHV3bwu5uXoeo2pmvA\nOP06Krf4KKaGHI1SOHkC26h/DCcHnkHUnuPGGwfpPR2n/2yCYrYfraRQKHg5sn9oSlIjn0ixp+t+\ndnc9xImmG7FyMw3ojLyzRorzLP2dbqbUly0yUiixNhqc0f++NOTj46u7UESRb50ZZSDv7OOlJqoA\nDbfdjndp/dE6UB1tsxCiuqJhKV7Jy12L76g8Vv6sSXet0y2bw5NZHu8bxyOJvG9Z26yJTa/s3Eus\n60T1TYu5ElXbLfUtX1XNNT9fVoz0fJGRni9iWzrRrrcjyQvL9L1RsShgks0/yc6Br/G9008BzkL9\nrmWtdPo9vD6R5tXxN94F6A0tw9cQo2XFh1BmcfwFKk69smzg93rIix6MRAJL0zBV529ev0Nmy+W/\nSV8bNqDZzuKlZYuki86p2tjsEE6vqyzVmiWZbp+s6nGCDV/A+b2lPcjDH9qMKAkV50DJ58OMNNNc\nSjJYUy5Y0Ew8bsComsUppS+i5EWwTSQgWZx5TB49+X2+dvzbfP61v2I455h42LbNuVQfzd5GQuob\nc+xQPWi5AVIjPyEz9uoVfd9SfhhDSxBo3EygycmFGVqc7vAiQmqQXnHUIar25Wuv16f1qAIEgs45\nql2lADZX0An4pgYkPo98wdLfbEHHo0j8wcdupKs5gGnkKWbOAhD15SjpFrpRfyaebduMJHIEVYeg\nSersowjmitn6U+cCWfGjSBbxVA7btvnDL+3m/zx60GmfsG3Gkw5BHUvOf8yIbdtMukmsaMjD4bMJ\n2hv9fOTtMf70k7cCkLedwL2eaVEZkiDwsyvaafEqvDSa5MWRSf65Z4CiabEhGpoSbOmGRbGmRLis\nqGrTiGpaNwjIEnKNuhFUZN61tJWbWsJsaZrZV9c/muVY7yTrljXytjmMH6r7Wdz9udRJIclNnngE\nnbbsOaRAdV0MbNiId0k3oWj1Pt88cghBVQnddMucXt8fqrZYrFrrkPRQgxetaMyqlJYfD7vn5ZtF\nUTUtE8M2UWqIKoBpOglIy8pR1Hbymxu6effSVkKKjSRFKJieivPufKBpBjYOUQ1m09xwaDerju8n\n8/puwIlt3tLRyKam868lKxv8/Pbm5fzsig7WRoPIoki4ZsZu07Q2q2KxSlQDm7Zgl0oM/d3fkvqP\nn6DHq6Tctkxs15Ctrek0WzYeZ89Lh7CMNMlkiI7FEUzD4slvHazEFqlUCVtwYopJXztmbmb5r5lb\nAFGtM57mlTGH0G+fxZSzO+Tj9vYIhm2zZ9xJ3p8v+XW5IDcvnKhubt3An7/lj1gcqpo5hWvaKDa7\n58ajZ0cpWTYPd7ecd82ttlQZFLV9896f63jjY67R3l8BzwHtsVjsr4A9wF9etr2qQSk/hGUW8DXE\n8EfXX4m3vKKIehowzSHA4ly6n5OTp0lpaRRR5MMrO/BJIs8OxN9wqqooeWhZ/gF84RUXfB6ALJmo\ngkBaCWAnE9iahukqlb6AE5R0LnaI6qSvHV3yVoa/F9O5ipHSTKJaDSgqJjhu6e+ajR1sunkxD31g\nEx6vQiDoqSiqAIW2FSCqjPY7pFI3LAzTwuMGaIpZRG6I1HwWJ4jxCgIZPYtuVd/btExOTJ6q/L5/\n7BAA59L95Iw8SxuuHTUVwNKdm3HZtv9KwTRd9c7bguJxMu56MY4oiHQE2plQNRRLw0K6bCMkyj2q\n5YQHVGfj6qXzDzq/XMgV9UrZbxl+r4xuWOjnUXnTuRKNYQ9L2x0yU0j1UM4xBtUcomDP2qeaypUo\naCZ+1QnYLkWSsExUmxegqPp8zrUfn0wSTxUZGM9x4HSc146PkcqWKLnfw9gCSrQLmklJt9i0ook/\n+9StfP6T2/njT9zMXVu6aAiqCEDR/d6i6vkDQ68s8XOrOvFJIk/3TzCQ09jaFOLeRVMVpC88dojf\n+6dXsSwbw7RQ3FNrrFDtkbNtm3TJqDvybGNjiHctbasQylqMuaR9w/KmeRmt1EJyFdtLbaiUy5QI\nhT3cK7yO18wj1innDTU3INgWsmgTGT5CcOu2ORvVKAE/klnC5xHoWBwhf6IHX8EhZulk/XOjnIAq\nGy9drYTUpUbJct1wJeecXRldDkDUY2NaKUq644ib0zPc2NLAXW0ZcnnH6b5sZjQdtm1z/OAw8Tr9\n4HrJwFJFbKBpdJC3jp5FNXQyFxhlMhfUeoBMLwMtxwGqZNFw+1uQG5vIHdjP6L/+C2c/+5uk/uMn\nAFju/cUbWo4nsITOjnHWrnwFgHQ2wvbwIIuTR5icyPP9L79KSTPI5Kprvi556/aplhVVaZ6lvxZU\nKhYyusGRySytXpVlodnP9XKf+2BeQ+D8FR6XC5LPhxgIYMTnT1Rhphlao0dBFgTWRgK8rdNZJ03b\nZmNjkM11EnG1KPeoAmil1xe0P9fxxsaciGpPT8+/AZ8C/gdwBnhHT0/Ply7njtUi1LqD5mXvW/AA\n6jcyarNK4/kJ/nrfP/Jvx74JQMSjsDzsp2haZPSrEyBfLGoVVdmyScsBBENHT8Sx3IAvFHYCldbO\nEJIskvS1V8p+AQxBqRDVaLPzuNfnqq01iqruBhfl3sJIo58dd6+okNpASCWfLWFZFnrJ4HUzxrHW\n28j1DQBOfyqA6p5nqqVNGWgtujNave5pmNaqjsG9mQGKpsa21k2Igsi+8UN8++Tj/MXeLwBUSlev\nFZiGU95UdkO8UrAMNzMt+5DVKCBgaE5G3Cd70VQByXSSDcU5OtbOF1XX3ypRVa8iUTVMi4JmziCq\nF3L+NUyLXNGgIVANZPKTTtmvGliEKNg0eGcv/x2eKPerOa8vShdPVCfSC1dUA67yNpZITpl//I0X\nTtE/Vv19IUQ16SawIiEPsiTSGvVX7jei2w+su3fL6eNlUrkSp88eJTHwDLarujZ5VT64sgO/LPGW\n9ijvXtY2paeubzTDgdNxJjMaqVyJLz11jC896iS3hnLVZJpmWpQsu2LSNFeU1eWWyPy/5zIqiuol\nnKVq2zb5rEYg5MHMZhF9/rp9cGprCyvie4hN7kGyTRpuu6POq82y3wE/G0Z+xFvWimAaDP/DF+Cg\nUxkymyFaWUENud9XqSZ5k8+VGB1K193ujY6S6VzbqnsfXhJaxGdv/DSf3vwLFPLfRivtASBedFx4\n80YBw3T8Ac7NMnlgz85efvRUDy88cXzG30qaiekm+Pz5DP7YGsch9uwZSqOjM54/H3S6PfbYNpGa\nZJFhmPSdjiPYFj7JwLt0Kcv+7M9Z+rn/n+b3vM/5XMePAWC69xfZ00jryg+jBtfg9TqJoaaWJWR/\n8ASrU/vpSJ8gnjJ47K+eIlWoXrclyYeRmen8a7rlwPNRVMvl+mVDpdfH05g23NJav3e3jEUBL6q7\nbSwSIHIVFFVwyn/1+MQlEVECisSvb+jmZ1Z0VFokIqrMw90XbluQhDf3fPrrmLvr7wbgD3p6eh4B\nfgg8EovFYpd1z2qgeKIIwjU1SWfOWNe0hl/f+susbYpRsnRsbI4nTpJySVCzuwjFZxn4/EaHIAgI\nooosm6BbpN1ZqmYqheUOxvZ7ncVdliUamwPk1Aa0GtdBXVLJqhH8PgmPm0n11Cn9LRMMdRY30UDQ\ng21DPqczPpLFsgXS3ibMsWFs26boBvySe+Pw+eQpQVRZHfa6N5FJrUriehKOmrqldSOrIssZzA7z\nQv+LNHoi/OKGn+PGtvrueW9UmIaTNTausKJqua6TouxDECVkNVLpk/VJXhAEwDnO0w1QLhX0ukRV\ncv925ZWWsuIZ8E49r/0XIKoZt28rVJ4VaBQoZs6i+DoqhnTNgcKss1SH3bmSflXHsiWEWUZQzQeV\n0t8FKKqya/CSyWU40e+cl6sWNTCZ0fjacycrzxubRTU7H8plv5FgfWduf1jFVAQEoGGaovr150+i\nJB8lO76bUm6g8viKsJ/f27zMcemdFng++1rVbTyeLrLryChW0UQBhmrGhZTX/cgFHJKnY6JCVBfm\n3gpVRbWY6aNUuDiSUUYhr2Pb4A96MHPZSn/qdChNzXQnj9A+fhi5qQlfbM3c99sfoKkwTFQqktm1\nEzOVwqc799OhviQHdvfPCK6rpb/O91WrqD75jYN851/3Tpmzeq2gZDokTJGq5+yS8CKafFE+e9On\neefy+wGIu4pzXs8DJSKqM/c3N33MUiLPay+eA2BiNIsx7e+lkoHprpv+XAa1o4PwLU7pfGb3LoxU\natbe0QthSaMTO3h0e0pP9uE9g2TTGt3FM3jcygNBEFA7Oom+/QEEWaY06pDvqfcXmbaV76PEJnL5\nRtpTSaxikZb3vJ/7fuk+OoQECTtMb9RxuI34wRZECsnZFdX5mCmVE1embWPaNrvHUqiiwJbm85dF\nS6JQGeuzoy1y3udeTijNzdilEmb60iRxoh4FWXRGFH3qhsX86rol+OQLk9C2QCux5hV8MPYe1jZd\nMVpyHVcQc2V//wR8GaCnp+cY8DmuoOuvqFw7vX3zhSAIrIwsY0mwq/KYjc3ro/sBaHKJWbxYfyTD\ntQBR8qLIJoZmVIiqDSA7p5/PWw0OVY+ELUgU5eox1+RgxfG3jHo9qqVpiup0BN3epVxGY2zYyYqa\nokqgkCOZdcocAYSiiWgZhIJTg0PRJc8e9waTdImqbdvsGz+IgMCq6HLuXHQbTd4oP73iQX5/+2fY\n3LL+mqsGsFzXTdssYpnzn1lqWTrDx/6O9OjO+W3nZrwlV72TvU1YRg7LKOJTXNMEl6gWCpfnmqi6\n/laPfzn5UboKiup0x98yyorqbH2qGXfMQthVVJ2yXwt/5AZkt6y6KVCYtfR3eMIlqopOyVIvyTkc\nvwhFVXKvP69s8NIhx7Dsk+9cR2PYM0VFPTWQ4jcfebmiks4FyUyZqM4so5vUdOSNTYghlbAqzzAu\nOtFfHV8xmpia2Kn3nSWzGq8erRK/RLpaYtkoy8Q1naLbNzzh9t7Nd/RZRVFtWDhRlSUBAZvc4DcZ\nP/3vFbX4YlD2CPAHVcxsFilY38VXaa66yod33D6r+2g9lJ2BzVyWxA8cd3a/S1SP7Bti5wun6T0V\nn7JN2UyprKjW9qhOjDnEZODs5Jz34Y2CkuWsAWUzpVp0BTtYEnKc6BOuolownHOxOyijWzafP3CG\nr50apuCej2dPOKWe5fvvyODU813XTEyvc6z8+SxqRxeBLVsRFIXJ53/Imd/8NeLf/+6CPkur62Uh\n5av3/GJBZ8/OPjxemaXJwwieqYkmQRRR2trRR0ecXnbTJapSdezVyi0Pc8Nt/xV71BmJEti4Cf/S\npbzjNx5GEpyEhoBNU9T5zPnUTDMlcwFmSrK7Nhi2zYlkjpRusKUpXJkNez48uKSZ9y1rY8V5SoQv\nNyqGSgss/z0fAoo0J5IKoIgyn7vnM9zWdQv/ZdMnLvm+XMfVx1xX/0BPT88z5V96enp+iDOu5opA\nUi7exOONjs6gM/oh4mlAFER2j+wFqqYBE5dxbuTlhiCpyIqJVtDJuES1JPkQXat5j6d6Ey2rWIWa\nY55yR+FEa2YBlh19czUzFSturbMpqjVEdXykmgUMYjM4kaWgGQiAWdQJliZRG6b2Ruju5eIXnX0s\nE9Uj8eMMZofZ2rqRoBJgY8s6/njH73Bv950o4vyUkDcKyqW/sDBV1dAm0YvjJIeem9f2plnNeAPV\nPlUt7iiqgCk4AXNhcvbh6xeD+oqqcxynKwhXAmXFc7qZkt97fqKadl1qw+61Unb79UfXorizjyO+\n85T+JtzSX9WgZF6aPqh4qogsCRXyPB/ISpmomhQ0k+YGL41hLx+4e1XlOYqb/JrMaPT0OQSyoBk8\n+uPTjE3Orogla4yUpqO2ZzRTR1FXhCrRHB0fn/H36Xhh7wCmZbNxhXNux2uIasQNUofd95xwE5T1\n5k2eD+OpImG/gmeWpN1cIIkCDT4NbB1TT1NMn57X9oO9k+x9pZcTR0YrlQhlj4CAVwTTnGKkVIty\nEAwQ3nHbvN63rGpldu9CHxlB7ezEq2cRavwfZ1dUp5b+1vbB952ZSm6vBUwv/Z2ORp+zDtSW/gK8\ntT3Mg4ubaVIVDk9m+d6RQQr5EudOTSAIsOMex5tioDc55fVyWW2Goir5fAQ2bsJyezsTTz1Rd19q\nj4ltWejTrqWIR2H5RInQiVQlKb13Zy8lzWDbjm6kYgbROzMBpra1YRWLmOlUNRFap99eHxsDSUJp\ncq5LRZFYvNI5D20EAu4M73xmZu+u4ZopSfOYo6pIDlEtmRbHXfJ7ITW1jGavypbm8FVNgCvtjgt8\n4onvY2nzT2Zfx3XMFXMlqmOxWOxTsVgs6P77BeDS1ALNAZL85lVUy1jWsARZkLi5fSvrmtYwkB1i\nKDtSMQ2Ia9ewoip6kCWTfLaEFnaJh+RBEi337zOJal6tut6Z7t/LRkrgZHQDIQ+jQ+nKDa5igjNL\ncFYmqqnJQkVRBZBEhcGxLIWSgRfAhqCWQGqYWlaTtxyS0uFzHh/IDPPk2R9WeorfvvTuuX4lb3jU\nEtWFGCrZVpX8pEdfnvN21dIsV1F1HaUNLYHPtaE3BFdRTc7MbF8KlGcB1hLVsplSORlyJXEhRbU8\nPiWeKjKayFeuh3SNomoZBYqZMyi+dhRPI4JL+j2yOess1eF4npBPwCObFIxLRFTTRZrC3gUZ/JQV\nVZ/bM7vZDSJvjLWwaUUTLREv77i1u/L8Adfs5d+e7eGpXb38eP8QsyGZcb6reqW/yVL1+7lxmrGH\nVjIJKdVgPZk+/zgnTTf50d5Bgj6Fh3YsBarl0ABht8VlKOc8Nu4S1fkoqpZlE08VL6rsF0CWRJr8\nVaU6m9g/5221os4T3zjIqz85y/OPH+PLf7OTH37vKKfcGbVe2V37Zyn9Fb1evMuWE9x2I2rL/Mbr\niK45n+72RLZ++KOI2BVzPpjZa14mqsXnnkAAshkNy7KnmC8N9iavWKIqr+cr5bgXg3LpryrVT3Q0\nepx72YHxw/zp7r9i37jTJx1WfdzeHuWOgoComRzKF/jSI68wMpCmtauB5aubEQSnlLqM8ZEMZ09M\nIDY411BIlhBdhTPklv8CiP4Ao//2ZRI/eBrbssi8/hq9f/JHnP3sZyicOY1ZKDD4N3/J2d/5LVIv\nvzRlf9fIKmpGJzVZIJ0scGjvIKEGL+s2toFpVt6vFkqbQ6jGvvZVcqedXlVRnnltlMZGUZqbEWoU\nzfIkAgB/2NmmkJsZi+nu+B1xlsRLPZRnh6Z1k9PpAl5JZFFg4T3lVxrh7Tvwr11H7uABBv78zzAu\nUQnwdVzHdMxV7vk48AXgfwE68BPgFy7XTk2HpFwx8faqodEb5Y92/DYhJciBiSMcmjjK7pG9PLzi\nAVRRIH4NK6qi5EEULbBNaHSCDlNUECUnWBFqVMfySBCjayUkNUQsLKaOpgGnZKdjUQOnjo2RmiwQ\nafRXSMRsPapht6xr14/PAI4x0+REHk0JkxgYIxRYRDnPGixNIkdWTdm+hIACNKnOfrw26qjePtnH\nu1c+RJeril9OGKW0831K9XvpLhXKpb8A42f+ncbFDxFs3mdxkZAAACAASURBVDrn7W2rejMvj0MB\nKKROYhp5gk2b6r+v68pYdliuVVS95f5mN8gtLGB8wlxQLf2t06N6FRTV2Yiqv6b017Js/vgrr5HJ\n6zQ3eNmwvImMu13Yr5JPnQDbKfsFJ3kE4JHMyvNqUdAMJjMaW1c4x6GgX3x/qqabZPI6i1sXlniU\n3NLvHeui3LRtPVtWO0RVEAR+9T0bsbGRRJE7Nnbym4+8TP9Yll1HRth1xCEs4+fpXU24Kkl9ouqQ\nmMmDE2x/d9eM7TrC1Z61YiGFbduzKh2vHB4hVzR4aEc3HU3OajMwVt0+5K515T7VeFFHFgQa6rj+\nnu+zmJZ90URVEgWaAtXvrJDqwdRzQH3Vx3GDHSGfK6F6JCzLJrahnVDYw8mjYxWSCuAVDQyYVVEF\nWPy7/x0WYNQi+av3icDGTfhXx5CCU/d5egl/mahmX3iGSOd9JMY7eeGJYyxdVVV2DcMiMZGjteP8\nLqSXAl87/m16Jk/x+dv/AElcuCpeLv1V65T+gtO7uqJhGadTZ7EKFgHZz4amG/C5RC6X1AglsqRW\nNiCtb0Y6neRgLEipf5xoa5CJ0SymaWEaFs8/cQzbhkCrn6Rp0thQPbbBzVto/cjHSL/8HxTPnCH1\nkx8DkHzhOYx43PUegIE//zNCN95E/rBDmCe+9Q2Cm7dUyrnDUWe/UpMFzp6cwDJtbnnrMgTD+Zyi\np56i6hDV7J7Xkb2NyJsjCOLU55m5HFY2i2/Z8imPr1rbxv5d/Wy+ZTFe2QbGKNTxRsidce5xnq5F\ndb/neiiPZOnNFEhoOmsjgQU7dF8NiB4PXZ/+dUb/9V9I73yZ/j/9HIs+89kp1RDXcR2XAnO6+/X0\n9PQBD13mfakLUfYj/Cdx9Yp4HBVxQ9MNeCUvr43u450r7qfJqzJRLJ03AHojQyiPqJENGvwecpIX\nU5CRRbcnVKjpBXTJQS7v3Az8ikXWtdssO/6W0bHYIarD/SmHqNYZK1KLlvYQ9zy0htM943i8Chu2\ndfG9f32NrKeRQv8AhaUd+N2se0hLIIenzjLTLBsFCMkeuoIdlMwSdy2+g1vat02xSL8csG2bQqqH\nibPfxBteSeuKD17W9yubKZWR6H9yXkTVqiGqhhbHMopo+QHGz3wdAH8kViGjU7YzCs68WldZkr0O\nUTWKcXwB52fLneNRzF2ecqN6c1SvpuvvrKW/ZaJaNBhPFsjkdSJBlVxR50f7BivPCwdU9KJTRucN\nOopjOdHhkU1G6hDVEdc4ZlGTez1qF1/CfjFGSgCyq7IvaVGIdk1V2URRAPfajYY8RIIqZ4bSnBxI\n4VElTNM+L1EdSxbwqhIh/0xCnnLLDI1MCW2aoh5PF2kLVUuKVbHAeKpI6ywk8bk9A0iiwN1bF+H3\nyHhUiVODVSVCtWxUUWAwr2HbNuPFEk1eZV4BbO+Ic+22Ri+WqFYVVX90A/nJQ+QmD0Jne93nnz4+\nzo+f7pny2Op1rSxa2siNty/l7IkJfvDYEQC8tkYWZjVTAre/dwH3O9Hnc7azbaL3PwiA2tHBqvHd\nnGy+Gaj6GZShFXVUVUQANoz8mBN3fIqTR8foO+Ooml3dEQZ7k2RSGq2XPx/JmVQveaOAZpbwiws/\njhVF9TxGaL++9VNYtlWXEGfTRYKDOQqrI0x2+Hj7TYs5e3aUg4ks3SvCWKNZEuM59rzcy+REng3b\nuthlm3gLOfxt1fNEEEUib70TYzJO8cyZyuNGMkn49rfQeP+DZA/sY+Jb3yC982VEr5foffcT//53\niX/vO7R+8CMANLjn9KmjY5w9OUFLe4iVN7RiJJzjJHhn3ofVtrbKz3JrFLApHDiC97YqqdTHnGSW\n0to2ZVt/QOWjv7oDgIGzTul3UZvaq23bNtkzZ1FaWiqEei4oJ5/2xZ3qrhXhi3dVv9IQZJm2j/8C\nUkOEyaefZPK5H9L6gZ+92rt1HW8ynLf0NxaLPeH+fzYWi52Z/m++bxaLxcRYLPb3sVjslVgs9uNY\nLLbyQttI8pu/P3U6FElha+sGklqKU8kzGGYG3bIpXaa5kZcbZfVGlk2CisSkEsIUZUdlZZqiqpTd\nVU0kWayU2/gDSsXxt4yORQ6RHOxz+msqPaqzqA+CILB6fTsPvGcDd79jDS3tIaIhibwSwh4fJa8Z\nlG9z/lKqTumv21OLxe/e/Ov84a2f5a2Ldlx2kmqZJYaO/A0TZ50S42L6FJZ1aRX2/ORRho5+gfzk\nUSyzhG3pqP7OGqdXG6PkBNS2bV3Qkt52e6PKZDQb38vE2W/h2GjZaNn+utuZRr5S9gsgKWEEQUav\nLf0tE9X85akyKJVMBAFkpbo8VonqlXf9nVVR9VZdfwfdUTL33riYv/70HXzsgapLaiigYrpzccv9\n/s41J6LKJsnszFK2kbhDvsqmkhnt4pOFoy75bV6g0ldWVC2z/nzHWixqDZIt6BQ0gw++bRWtUR8T\nyfrbWbbNaKJAW81ImlokSzrYYGrmjH7gRFrDr1TPw5CnxGCd+ZLgHMehiRw3LI0SCXoQBIGmsLcy\nRxHAtKDD72G8UCKh6ZQse95GSi8edEqct8XmVzI7HbJUVVQjHXeCIJGL75/12j+yz3nfcM1InLZO\nZ40WBIHlsRY237KYaJMfn+329J2HqC4Ugiiidi3Cv3YdvlWrAVBaWlmSPMo7HnTmWU9XVEuaQbmA\nQrFKPPSBTXR1RypK65LlTgtCNl3/HDq8d5DxkUvTM5/XC6TctbZkXVzLT6VHdRZFFZxjM5tqm0lr\neCWRW1obyOgmPxioGudk/M42Lzx5nLMnJ+jqjrD9ruWkTZtANo3a2Tnj9TydVXK4+Hd+n+X/83/T\n/rGfR21vJ7x9RyUxEdi0mcYHH0Jpayf5oxco9vUCVaJ69qSzH7fetRxBELA057hMV1Rt28ZutPHG\nVtL6kY/hWeEc/8T3n8LSa/wtxhy1X2mbSlRr4XOrLQrpPJnXdlceNxIJjHQaz5Lu2Tati7KiWi7v\n7w5eO2W/tRAEgejb7gXASFx7fdzX8cbHhXpUf9H9//3AXXX+zRc/DXh7enpuBX4b+N8X2uA/Q9lv\nPdzc7qhXr47sZTDrjDzIG9dm+a8glWepmnhFgaQSwhRkJMnCsoQpo4em9wX6ok6ZVa3jbxnR5gDh\niJeTR8Y4d2qi2qM6i6JaD01tIRBEQrpG/2iG8payVUKOTCWqWTc+Uy4yeJgvzFIK051n6nEVMS3b\nd+leX8+R6H8CQ5tg4tyjxHsfA0DxNtO1/jeIdDo3oVxiP8mh5xk8/JeMHP/785LVcumvJ7QMgOTQ\nc9iWTrB5m7P/uX7XhbH6Xdq27SqqVSIjCAKypxFDi+NzVUDd6yQ4irOYCF0s9JKBokpTSMvVVFQz\nsxDVqKtMHjg9USkf7WwOIEsid2zsYElrEJ9HIhpUsQwniK51UBclDz7FnOI6W8ZQ3CG+Te7T04WL\nJ6qnXIfQ5Z0LK50sG6DMhaiWy4tvjLVw+4YOWhq85DWjbj9uIl3EMC3aGusT6GTJwCMANnzvpbMc\n7626v06kiqiyiY2IKQQJeUqMJuort0NuMmFRzVo2XV02TItOvxcbODzpHNPmeRgpJdJFDp2Js6wj\nvOAS6zIkSaDRX8QW/MieKP6GGHpxnFzKWXts23ZLgWFyIsdQX5Ku7gg33b608hrKNL+AW+9awc/8\n4s2Qd7a7HEQVoPu//yFdn/71yjUsRx3TIKnovO9MRdVAEarXtqyIPPi+Daxc20pLe4iOxc69IJOa\nee5l00VefPYkj355zyXZ95F81f5DNy9ujStZFyaqs8G2bTKpIsGwlx1tUSQBMrqJKgq0+VTSWNhA\nYjxHqMHLvQ+vpWjbGIJAIJvC0zGTqKpdTum80taOb8VK5JpksNzQgH/tOgCC225CkGVaP/hhsG3G\nvvZVbNsm1OCtrMVrNrbT1e0cV6voVNdM71HNjL3CRN83CHxgE5G33oktGE7SKZ4ivbPqSF9WVNXW\n2ZM7PnfMV0n2M/wPX2Dy2WewbRut7xwA3u6lc/1qAQjXJNQFoMV3aXwArgakUBgEASN1ZcfZXcd/\nDpy3nqunp2fY/fFfe3p6brgE73c78Iz72rtisdiNF9rgP4Pjbz2siCwj6onw+uh+FMUpPckZOlGu\nvaxbWVVTVR3Bhj5vG1EtjihaWPbUQGb67Mqyic30sl9wyv3u++l1PPbVfTz/+DEibs+XOo9+rpYl\nLRw7kcaHyJ7BFItskGwdSVVQO6bWeBVMjYxlEbAuHChfSti2O4y+5Ra84RWMZ3spZs7iC6+4JK8/\nOfgsllkk1LqdYuacO8YERDmAKHnwhpfDEKSGf1zZxjJyWGahrnsiVEt/vcGlFJKOgUW06+0EmjaT\nndiLlu1jcvAHZMdfI9y2g4b2t2LbJmDNMLqQvU3oxTG8rttvyWvizdpoWv0KA9uyGP/Gv+NfcwPB\nLXMvVy5DL5kzAmzPVRxPMxzPOf2C00hNa8THrevaeeXISKWstsvt4xYEgd//6I1ouokiS5h6FkHy\nINaUAAqSB69SJJ6eWkJdTJ8hbL5Gd9RDxO+lmINUXsKy7YvqoTo1mEIQYPkCe/zKiqo9B6J61+Yu\nsOHBW7sRBKGi4k4kiwTaFb79k9N4VYkHtndXiGV748xz2bJt0iWDBjeZdqI/yf/893186bcd47RE\nusiSBhNBVJHkEEHPCCNj9U2+yqp3V02v/Y1rWjhU4yZrmBZdASfYPphwiGrLPBTVlw4OY9vw1s0z\nScJcYVk6oqigiCYRXxFLcl4r0LSZfPIo8cHd+FvvJzP+KsnBZ2nouJsjhxwlat2WTpauambg3OSU\n/s7pMCYdsn++HtWLgTBtxEc56SgWnO9UrynhNk0LQ7dQ5JqkmaYhe73c+861ABRcY7Jsema7Qabm\nsVxWIzDLLN65YjhbJarFxDjJ3Qew8nmi992PIM+vBH8upb+zbqsZ6CWTUNhDgyqzuSnMnok03UEf\nfllitFBCDKtIBYMH3rMen19lwDUBC2TTyK57bi3U9g6CW7cR2LCx7nu2vPf9ZLqXEtzoeBgE1q0n\nuGUb2X17yOzaSfjW23jvx7YBNg3R6vVqlxXVGtffQuokyaHnAMglDhLpuBvLLDjtZLLM5A+epuGO\ntyCIIqVK6W/9snaojuQRulch5fcw/s2vY6RSCIo7232eRDWoSIiAhTNDVJnHCKY3GgRRRGpowEwm\nL/zk67iOeWKuq96BWCz2c8CrQCVd7PauzgdhoDblYsZiMbmnp2fWtGHrov/H3nuHR3bf572fU6fP\nYNAWZbHAVnALuezLIpFilUmJIlVoFVtWJFu2ZSlOfHOTOL6OnTjRTey4xDeyk1iy5FiyJKqSokRS\npEiKFJe9bO+LxaIDA2B6Of3+cc40YNDBNWnt+zx8lgBmzjlz5pzf+b2/9/2+38tpbvv5JKs3b9nH\ng8d/jIy3KhqSabtA52I99yOa3WQmIBQsYokih6LbaJY3sEE6jUP9Z2pprU5cgmEfTd7DqHdzS8Nj\namuLYN5v8eA3DjA15qpGXd1NCwYqzcW2XZ08+5OziJJKOqezzbGQLY0Nt91CR2/9Q8sZscjaDhGr\nQGtrqE4JfjMRi/mYAIKhAJ29u5kekLBKQ+vyHWVmTlNIHiYY3cj2ve/HsS1GTz/C1NBztLRvoqUt\nguOEKCT6sS2d1o37yKfPkxh+gXCgRLipsVXKzEAKaN2wEYnr8AXidGx2J/cz5zopZofR8u7wkZnc\nj547TeeW2wEIhqJ1n01PdVJMHac8vzeCAmFbw7Dm3w+liQmMbI7Uk0+QevIJbnzouys+J6ZpEwgo\nddsueG01REG4YPcguAmuo4k8G9vDdHXG5v39Nz+0lwNnpsmXTAI+if6tbV69Zj1Gj+Tx+WN1x55Q\nA/jkPEXNJBj2EwoozI4fYOrs1+mPO2y/RiDij1ACCoZCOBKYVye7XBimxeBEls2dMTZtjK9qGwCC\nqCAKxpLfQVtbhJ3bq+rI5o1N8NoIr5+doWNDhB+94FoJx5NF+je5x7O9t3nedmeLOjbQNEftaGuL\nYNsOw4k8+5otFDVIINJMRh8nX8w2PL6kZ7Hevb2t8vf7btnBVx45UXmNz6ewu7uZ75ybZNwLVNrW\nEaMtvjShs2yH/UcnCPgk7n7n1koq9EpQzE1y/MX/TqR5G53hDkQHCG6irS2C07qX9OgjzE4cpGfn\n+8mMulbf9PhTzI5fRjiygWtu2IwkiXz4k9cuuA8zl2dg/7NIoSDdV+5BXqCX6npC3NTJFBCmRLmW\nufwd5LzermqNU6bJD76a79BxHGRFpFjQ5323k8PVGuOZiRx917dW3uPYDqK0sudEatitt9x1tkjx\nG39M0XOuxHs6aLt1vpFtsXtBnvQ+a3NsxePWxJg7VWvb4I7H9wUUTr14inf0tZEs6RyczXLD+3ez\nMx6my1Ochz0FOJxNowej/PV3D/E7H72Slpp+vu1/+HsL77RtN1y5u+5Xkd/6Nd747GFmvvttem+/\nibYGKdAzA+453n9yll/65QAYs4we/j6CKNPceSUzoy8ze/7rWEYGX6CFyK3vYvLxnyCeOUrrjTcw\nPjuDIEl0XdI3b5GjFs2tIaam8/h/9V9jfeMLJH/8KKHNrmuo+4pdKLGVneOoXyFVMuiJBS/oc+XN\nwGhLC8XhYVpbw/8oWSpv9/N3EQtjuU+yfcC1QO3V5wBbGr98QWSojwwUFyOpAJbUSyLx5vRLfKtj\nT3QPD/JjHMclquMzGdqFN7/Bc1tbZF3Pua67E5FwqMBEWgNB4LzYzLWijY1ct69iqTpZkCQBX1BG\nECAYVRc8ps7eJvZc2c2R190AmVS6sOyB0i2PddCUKAGrhCSoKLaO7/qb5+1vJptGtm26HJvJ8YkL\nova3tUVIev1Ci0WL2aSGGuqmkBliYnyq0rJjtZgefAGASMedTHuKj7/lVrpj12PLgco5aNr0YcBd\n/TUdd2I2PTlC0WhuuN1s1lUuMlmLYNudAJVthdvfhV76IbZVon3bL5NPHiGXeJlzh78OgGEqdede\nt9xJem7WDQQq+RwUS6Nk1F872tgY5//g9xBkmawax2cVV3UdayWTUNhX9964t2CSy2oXdDyaShYo\n6RadzcEF93vPDX186+kzdDSHmJmZXx/p2BaWUUD2tddtw3ZkZMEEHE4NTNOsDDE9+F0QFU5OhOhv\nTzI74aZvpks+hkdTtMRW5+g4O5rGMG36OsIkElkcx+EHA4+xObqJy9p2L70B3HtBlPzopfyKv4OA\n7I4HD/9sgKdfdeuj/arE84fGefHwBABBRZy33fNZL0yI+vFkZDTF4YEZhiezBC+zAQXbca+RdHqm\n4fGdGXZVxIAo1P39Lz53I4++NMTjrwyTypSQi27Sr+kRFLlkLuvzHh6YIZEsctPeLnKZIo0rZRdH\nZvIAjm2SmT5BOy6Bzhidlf2r4a3o068xNnyWYr665tzbcw5NuoyZmRz52QP4I5uR1aaG+5j54Q8w\nszlaP3g/yaINxTf/fioKXnr1+ATQSS5TqnymWW/cE0vVMzY1NIl/jnspHPWTnCnMv0bOVIPLDr46\nwqZtLWglg8e+d5RsusTHfuNaxBWoZQPTbqnPpaeLIAjE73w3yR8/xsijTyBcWm9CW+pZnc66n62Q\nNUiIKzvPQ4MuYZZV976QgH+31yVlRz2FOYWN4pcqxzBUHuNNncdeGuLg6Wl+vP8cd1zTs6J910EI\nEL/rPcw89H1Of/07tN77/nkvySTce+vcjMbBo0P4M9/CMku09N5HIHYJ/swMxewA4IAUJXDTLfDE\nkwx+8zvY2/dQGB1Dbm1lenbhXssAt7ynnx984yA/fPgMN+66CXXsAfLnzqG2tpLSRVjhuBSRJFIY\nxMT5Y8/bDqEwtq4zOTRZl7x9IVB7H1wkrP/0sChR7e/v7wK+AOSB54DfPXny5Fq0/f3APcC3+vv7\nrwMOr2Fb/+TRGdrA1thmRrwataJ54YNc1gOy12IkEi0xNOF+FsOw3D6qcxKd1TlJq7su76R3awuR\nJSbIN9y2ldRsAV03V7Sap6gyYcUi54vTqs/iBLqQHQO1Y74FqGiWyHiBSqaevmC2dMd2v3fBC7zw\nhzej5YbQcoOVdiOrRbneT/G31f1+MQIs+1wFytSSC76m3EdVaNBoPhDdRtfu38axDUTJhy+0kWDT\nTmaHHsbUZued13KLGltPoYgyJZ+NYmtk7UhdErY+5k4YNUfm5U334jNy7Fn008+HbbutFuZaf2VZ\nQhSFC16jOuzVni5Wb3j71RsZnsqxZ3PjRYNygrOk1G9DkHwIgoMi2WRmjmGXHkcQFWaV93Bw7Dj9\n7UksI4thq8wW/BTXUBN8esQlNdu6XVU4a+R4/PzTbI1tXjZRBbeMoBwMtRJ019SFlsOpPvv+S3np\n2CTPHXYrXDY0SMktt6aJz1EnJ5MFHtp/DlEARTTdllHePWMZRUq6iX9OCcLodJ62Jj++OddWLOzj\nmp3tPP7KMKZlI4kCHUGVkbxGUBYJysurD37W6xO7XNuvbRtYegbFX7Voll0Okhqr9E/WqI4NanAj\n8Bp6fgRTTyGpMaYTfuJNk4Q7DfTCGLNDDxNq3ktL770N91u+T2t7a77ZKNeoWpkUirqxzsJf7pXq\nK1THMzs/374difpIzRTm2XtnZtzzJNoWo+eTTI5lePqREySnXdKTmi3WtVZrhLMnpohEfbR3xRjP\nTyBZDm0pE7urg7b7P0JpcJDiyROc/6M/xMrncAyDDb/ySdruuGnR7Zres0NehfU355UThKPzrczt\nnh19qlRvhS7fLzHB4XjCPYeDE2vvr9l0+53MPPR9SmfPNPy7rbnHYYgyVvYApjZDpO06Qs2uxbh9\n2y9hWzp6cRzF14qkhAhfeRW5114l99qrWLksvr7NSx5He2eUu++/lB89cIj9QwH2BjppLo4T3rr0\nexshqkqQh/a3cX1qGWV7vZlKX3CiehH/tLHUMt9XgBPA/w34gD9f4/6+D5T6+/ufB/4C+J01bu+f\nPD57+a/SG3FrJUvWha+PWw+Ikg9JiRAOFSrNskVAkuy6mjmYW6MqI4rikiQVQJJE3vvhy/jAx1de\nkxiPKRiSnw7HfTCrkoPQYAW8aBQrRNXSl374Oo5DPnmUydN/h5YfXfL1C2+ovo2PP+IaGWr7k65+\n0+4DvhGhXAiKzyVEhrZwQ/pymNLc77cMQRDresH6w710XPIbtPR+YF4bnEqLGm0Gv+x3iaql4SDW\nEUe75E44C4pbA6kpC5O7YkHn2MGxeYFQBc+e6Q/MX8NTVOmC91EtE9WNixBVWRL59D27uH5P4/oq\ny3BXmucSVdH7zi/tTBAqPo4gSrRv/RinJgOMZ6oTjbzdAgjzEm+Xgu04ldrZs16Q0jYvqTtZctc7\n09rKwjdEyY9tlZZMnZ6L9qYAn//0Pu65oQ9we4Ru3xjjk3dfwi/dsYP33tBHsEFoUVp3SW3znDrR\nR148z2giz417WgEHQVQrtfg+2eRbT5/l2YNjnB5J4TgOqZxGtmDQ3dr4e1Q8e6hpunXXXUH33lhu\n4m86r3PgzDQ97WH6Opa3gJY4+w3Gj/9VZcHJcdw0bkltom2z66B4fWQDllNd+POF3DCcUu48lpHF\nIcLRYy4xNrIvoeddNVAvTrIQrKx3PUYunPIhR72wl2QSVZXqwpTSXo2yL1vt82o1IKphr0b877/w\nAmdPVF+b9cLIepOHcBz4/ldfJzldqGQmzEwtvrCiayZPPHSMJ/7Pswz8+3+Hf2yW1qSJZIO50S2t\niN/xbpAk9MkJHN3AymQonDi25Oc2vHwDZRW9WMu1t42ev81+BUUUGK1pEWY7DqdSOQTbplkWGJ12\nP/fgOqQhS4EAcnMz+vhYw787HmE2BBlBd2390Y531L1GlFT84d5KQGfzXe8BIPGA6+RZLEipFl09\nTfzCB/eAIHCo81aKcojw1tXlRbT63Pu7fL+/nVHukmClL9apXsT6Yinrb/fJkyffDdDf3/8kcGAt\nOzt58qQN/OZatvHzBp+kEpQV0KG0xgTAf0zIvlZ8xjkc2yCoSIiGgyjaOEsQ1ZVgtXURLR0xhqdT\nXNoe4XCu2st1LopmEdlb2zH1xSfYenGS5MhjaDn3oVnKDlQmeSuFUyGq7nGpoS4EUaWUXXGHqHmw\nbQ1B9K3o3ElqDAQRcxGiWg5TWgkBFkWFUPN8DVSUAoiSH6PktqgpSjlET7HVSmblOinMpnmj606U\nZYTtHD84zkvPnMPvV9jSX1WMxj3lr71BMq2iShhvUtLwQmiUFLtSVFrTzGn1Ve5v/L7dZ7BsiWH9\ndr7+0DSnRlKAH0FUcWwdS3Iny4lUkR09je2cc+E4Dn/xwAGODib5L79+HadH0zSF1UogVNIjqCkt\nvaL+0ILkBxwcW68c/3LR2RLihj0dPPz8IJs7o6heK6zbrtq44HvKClFvc4iP3LadQsngB/sHefn4\nFKIgcPe1nWgj7mJceeHFL1v8tKaP7V37NrHVU5I3L5B4LJeJqlUmqn4gs+zE39dOTmHZDjft7Vr2\nudRyg+4+jQyyL45RSmBbRYLR7ajBDoaFj/LI8UH+2eZqaJnsa0GSA5WAtELRTzoTwRY3ouUGsU33\nejVKCRzHbljHb2aziIEAorK6eufVQJAkpGgUM5VCaZYp1fQOTnuKarBm8bERUd20pZlzp6YxDIuf\nPnqSto4I0aYAxbyJaMPGzEnOtVyO4whcf8tWWjeEefibB5lNNA7XKiM5U8BxIEuI0kSCX5wwSbS4\n46bh9QsOX34F2//XlxAEAT0xxeC/+zfYhcVtqgCmtQZF1SPgkTkhbo5pMvJfP0/XLfdy3g6RNUwi\niszr0xkSmsn2k4cIBIJMz7jvn5gpUNTMVdVM10Lt7KJw9AhWoYAUrA8+K7ensRUJxR5BDXQuGPRX\nhr9vM8Gduykcd3v7LtaaZi56Njdz0507+OmjJznVF3nwuQAAIABJREFUuo8rt25hNUuYN3XG2R4L\n0vFPgKjKMXeMMy8GKl3EOmMpRbVSMHjy5Emj9ueLuHBQvXCU0tu0jyq4rU4AQsECTX4FWXAQRQdJ\nnkNUldr2NGtvibEctG92JwOJGa+lir/xA7VgltAF94FiGgsT1WLmLBMn/gYtdx4l4D78nDX0PXVs\n7xHorYoLgoQv3IupzS5JmJeCbWl1yuZyIAgistqEqSfJTL3IyKH/hmXWT5ocL21SWEVbhPn7E5B9\nLZj6LEHJjyUYCJ6lrXbCOTGlMRvsYjKydOl8uT9imZiWMTbkPmS7GhCyfwxFtaxiRoKrn9QvZP0V\naxYRDoy18uUns5waTrG1K8an7t6FGnAV2ubmPgBOjyx/AvLswTGODrpK3cvHJ8nkdbZtbKqQqFTJ\nPe+mY5Ez5k/kLdtiujh/IaSsWi6nRU0jbGgO8lv37eHj7+5f1uvLRLVJlbnzmh6uvqSqutx4aQfN\nEfcR6iYqu/fRR27t5Xd/6Uo+efcltMb8/PjlYfZ79uKFWvPIsrsdwxvjt0WDqKLAtujik+0ykl4g\n0HJb0pTLCaB6Lsstr/xht9ckUhjTlrCsqnotCAKhpmq/SMNwj0+J7HN/LiW8HViYpcY9Fa1sFil8\n4evI5FgTZjo1T1HNJF2iGjCqSbV2fr4KuqW/jU/+ixu5+d070DWLJx46hmXZaJqAz8yjWiWs5pe4\n6xd3c/m+HlraXeVuZoEU6DJSXs9iRxCZvPkWskGRDdPu+FnsasG2vX7j3r2TLQm82HMvI5mliZ/h\nPXfk1Siq6RKiKBAM14/h+sQ42uA5Wl9/EYAjszm+d26S7w1OIQF7X/8Zz57NUr5qHGBocu2qqtrl\nLvQ2UlVtTUPsC/Lx951DwHaT6peB+F13V7ffvnyiCm57nFa/xnR4E9ngyt5bRkCW2LLMe/ytjnKr\nIfOionoR64yVxpauzG91EesC1Vtt196m1l+gUgcVDhcJKxKK6H4WRal/CNYpqgsQxvVGa5dbvzQr\nuZZWf6gxcSuaRSyvx6e1CEEspk8BDi2999Gy6X3AGomqZ98qW38B/F5/0rXafx1bXzFRBVdZsc0C\nqdHHsa0ieqF+8uBaf4W6Y14LZF8LODbNsoKDBd76dS1Rnc3OH55so/F5Nw138jfRgKgqqkRbx/wJ\nv6JKF7w9jW64NYvyCpNDa1FRVBvUqJbh88f5+Lv7+bPP3cjvffwqbry0k2B8F7Kvhe7u7fhVqVJn\nuhyUSSpQqQHd3l1NLU5q1clMqoH992snvs0fvvBfGc2N1/1+rUQV4OpL2pdN6NKagSoK+L3z3+a1\nuZFEgXtu6MOxvTRoUa2cT59ssqOniXde1sXHbt+B7Ti8cXoaAdjcsQBR9RYjy6Sw2a/wH67axuUt\ny2vlU/AWXpaTylzKDjA7/KPKz7a3yFSuT/V5RFWSvGOy6++r5o7LK/+v6e4k2xfahBqqV6b10nz7\nr+M4WLksUuTNaUuzGOR4HEfXUWQB23KwPJt1arZIwC8iOyaiR1StRdTK/ks76N+zganxLPufPINl\nSvg9JTlcGsaIu6Ss9OKz+EWTmcR80js9meWnj55kejLLbE2IzvlAKz+4bg+v7XwP+7fcwvnTfr78\n3/dXkokty+bpn5wn74szUlr62jA9N46yCkU1mykRjs532+iTbvhY5+ggAA8PJXh1OkNHQOWXhCzh\nXIYk7r1QdmCsh/233Je1MVEtIW6uEr5AbMeythncuavSVkZpkEuxGARB4Nr3uGUqhw43XpT5eYJU\nIaoXe6lexPpiqVnk7v7+/lp/Ybf3swA4J0+eXGnq70WsAj5vEqPbb19FtRyoFAoWCJoituh+Flle\nhKiu0Sq0XESb/EjY6F4Yij/SqJ+iTdEsIYY2gFCq1P01gmVkvO1srUyo7TUR1Xrrr7vtKlENt1ze\n8H3LgW1pyOrK24X4w32UMqdrD7J+u7aBICrrFlOv+N1FhLh3L5TPSammRiql1V8vgmPhlErQwGJo\nesro9GSu0je1kNdJzRbp2RxvmNKpqrI7wbVspDUQx5VAMyxUZW37Ki+qSEr9xLZ2geKmK/sJt9Rb\n0yNt1xJpc9uMbOuOceTcLJmCTjS4tEqezlW/l0TKvQfK9alQT05TWpqeSP2+X554HYDx3ATd4Wo/\n4/UgqitBSjdpUqvXsU+RuGvfJpoiPlqbApSy7qRdkHwNj23vthYu3dLC4YEZOlqCBBdYfJurqK4U\n+ZI7voSWWNxzHIepM1+r+51tFrz61CFEOVgZq2Wx3o5cRrxjL4NHvglAUfMDNj6/QihwE4mzX8cf\n2UIpO4BRnKQo+tAKowgIIIhIYgws6x9HUW12xxDZG4t13UQVZHKZEm1N7th62BmjH7AaKKq1eOed\n25kcy3D0dZc0lYlqc9rk+OwpOs5Mk/j61wh23sGs3c3gmWn6trVSyOu8/Ow5jh90F2ASE1mCanXs\nLJ720cWVpABEKJ0BsDh3KsGlV23ktf3nSUy6x5Z0lg6sKSuqiriyZ6lpWhTzBvFN8/dhTLoLEM0z\nk4RLefRgmNu6W7ihvYnsT08zBeS8Bd3brtrIqeHUuhBVtcsjqmPziaqVTiO0udfrodStvDe0vJRh\nQRDo/PXPUBocQG3Q9mYpbNrWSiTm58iBUa64YdOCbqyfB8hN7vhuXSSqF7HOWGoGtAO4pea/8s/v\n8v69iAsAn9fXS7PevoJ22fobDhXxCwKq5NmZ5jxABUFA9ibmvgtEVAVBIB6tksBA0/zVfs3ScHAI\nKgEkJbJo8qhlZEGQ3Mbi3kq2Y6/BNV9J/a2eD8XfjiiHKnVmq4Fjm+BYq1JU/dH68Ajbqk9/dGy9\nzlq6VpQnz1HBC7PCvX6KmarykbbrU1sdQcLI1/w9WWT4nGsnNU2XqNq2w9S4u7AwNeb+29E9v18p\nVG3pFzL5Vzcs1GWmvi4Et5ZYmNcupGxVBZDVxp+5jO0eyTyzTFU1ndeJhtRKwq0qi3UqZjlMCeYr\nqrVBSZZTT5JE2SOD5ptDVI3SNKmxp3BsC82yKVo2TXPGoftv2cYdV7sTYbsSGlatUa29FwRB4GO3\nu4r0pVtaWAhzw5RWinyxnqg6jt0wcMooTc37nWUWsPS022MytKlCyiWxsaIqijLNm+5FDXaRy7mL\nH6pPIhDdRsclv0lL7/sRBJls4mUSA98gM/Es6YlnSI8/zezoQxCUkCJRHNukkDpRLW3A/V4d581Z\nkA3tcRNgSbvql65ZJMazOA6Efe5nnAl640uDGtVaKKrMnfftJhz14ZOm6Um5wUabJg3OHn6eiS9/\nEYDe1BEkSeCx7x7h+MFxvvE3L3H84Djx1iBdPTGmJ3MMDeeRLY1NMR0tkiHTO8R1H+ok2ToM3ng3\neHqGidE0r79wnkjUR9yYpiT4K3WkC8H0zq28QqKaWyRISZ9wF2dEUeS9D/4f/vWlvbyzI44kChU1\nLS8H+P1fuZqr+9sI+mQGx9ee/Kt6iqo2h6iaqRS5gwcwy0nE+cXHsnnb3bCB6CoTqAVBoH/PBgzd\n4sVnBvjKX+4nsQ6k/O2IcjhaOSztIi5ivbAoUT158uT5xf67UAf5844yUdXtty9RlZQoCArhcAEZ\nUCpEdb7aVW5Rc6EUVYANva2V/w/E51uqCoY7IQjIASQl7CZeLpA8aukZJCWCIAiV1Nu1WX+9iVxN\nMIkgCCj+Vu84VkecyhPqlYbSgNvOplahm6tw2Za+LvWplf2VWxzhknbTU+SLXp/LQk5DE/2E7PoJ\nppatEtWfPHyMR759GEM3MfTqZLhs/50a9/qwdTZWe8pW9HJy9YWAZlj4lLURVUNLIqmxSnujMmq/\n96WIatnCd2p4efVH6bxOU0ils9l1J2zujNbZl+sU1VI9Uc3o1YlOds6C0JutqE4Pfo/M5HNkEy+R\n8hJ/Y+rC41A5NVusUVSdOYs2G5qD/Nlnb+QXb9m24HbmhimtFLmSiaqIKLKEoc0ycuiPGTvyFxRS\nJ+peV/R+9oX7UPxuXZ1tFufZfoGKa2AuUQUIt+ylo//X8LqCVJwwaqAdSQnRvOm9OLaBIPlp3fxh\n2rf9MpG2fYCD1BdEikRIjT/F9LlvMXP+QRzHwdSSjB75c9ITz67qHCyF0O49iKEQzpRLdI6+McbD\nDxwEoCPkfteZkPeszS69INPSHubjv3U9zdJzRPQk/m3bCZZs7nx0CNsw8G/bTnNxnBv2hnEc+Omj\nJ9E1i3fcvo1f/NTV3PiOLgSPlKtWiWsvUzm98znCuwxa2kOMbjlM2/tztG4IMzaU4smHj+M4cOs9\nO2nFPb7RocXvR9M2EAURsUGo1WKoBinNfzbokxMgSUSuugZ/OokyO135m5Fwa5RzUpDejjCCINDX\nGWEyWaRQWv0zEEAKhZBiTejj9Qn6qaefBMuiGAxhWCJ57cLOk3q2uEr9sTfGKBWNilr+8wZRURF8\nfqzc2hclLuIianFh/GsXsSYEvAmD8TYmquVAnFCwiGhZBH3uDGdu3RzUTHouJFHdVFU7gq3zrbBF\n0wvckANen0+nknBZC8exsMwcskfihHUkqnPrPUXPXrVadcmu1NfNn4zMagb2Ii1ABEEgGK8m9M4l\nDo6tryjxdynIXkucoEdUdU9pKObc/Y6dd5XSbiXFez98GR1Bl0xqOfd7y6SKTI1lsW0HrWRWFFWo\nBipNeSvh7QsQ1Y5u9zsdPX/hwiJ0w16T9de2dGwzh+Kbf03XKt5zbcFzsbkziiQKy6pTLekmmm4R\nC/vo9Fp01Np+bccmpWWIePd+soa0WrbFQHqo8nNOr7/HqkS1uORxrAbles1SbpB0JUhp4fo+uyY0\nTKgoqvPvx4BPRhQXtsHLXj2osUrXTL5oEPISgkuZMzi2gWXmyCZeqntdIX0CBIm2LR9mw45PuMdr\nFijNDVKiWqO6GHnWNQtZEedZ5UPNl9G29WN07PgUwaZ+/JEtFRu5uDmEGAmSn3GbCBRSR0mNPk4h\ndQzHMSkkl267shoIskz4yquQSu7ix8GXhxFFgTvv28XGgHudFX0Ceb+IPjGOs4xSG9uxwWuNEr/9\nTgAUC17YG+LEFnd87g4WKr1IN+9o5dKrNyKKIoFcgqtGHiWgp+nKnCYVdM93Z2gDquR+l4ats21n\nO7btkEmVuOK6Hrp6mmhW3GtsdGjhXtbg9lFdqZoKkE0vrKgak5MorW34t3ht0s67moU2PEz21ZfJ\n+aPkA1Ek75ro9dolnV+POtWuLsyZmUrfVFvTSP30KcRwGNsnUTKlNfV7Xg3aOyN1lt+BUwnsdZyr\nGbpVCdSqxcDJBN/+yqtoa1wAWE/IkQjmRUX1ItYZF4nq2wA+LxnXtCFR1Pnx8DT62zABWPW3IEk2\nPrFAJOg+aBvVR5aJ6oWs96hV0fwNUvjKyk5YCXpElYZ1qpXgGtWb+AsSIKxTmFK9IiZ6NbWrnbTX\nqkG1ODSb5U8PDfJqYvGV0aauW2nf9nFvW9XJueM4nvV3/dpPiJKKpESIWVneFVDRZff6L9eoDhxz\n7WhdEZOezc1EvElfKe8e15njVcujrlkYhoUoCcSaA0yMZrBth8R4hkjMT2CBGsyezS5ZHhpYuC3P\nemOtiqqpuxPZMtGvRa2iOldtnQtVkejrjDA0mUVbwvqc9hTnWEilr9O9D3b1Vu/zjJ7Fciz6Yq59\ndqqQqLgTvnDkJR44pyEI7gR5biLwm62oil5LCz0/ymzevf6D1gyF5LGGC0JOzWKPIMggiPNs8MuB\nIAjIkrBqRTVfMitEVcsNu8ckBdByQ5VzZWpJjOIk/shmRMmHIPoAEcPIkUydAFFB8ZKez2eGsb2Q\nf2sR8qxrZsUBMxfmyVmsiaoiLvviiFYIsSeA1uweV7j1amR/K9nES6QnfuYd5/SSaeY5I8/jg0/z\nysQbTOSnXMK4DPg6u1G9Rce2jgj3f/Jqtl7Sjl10f6cpIue7VIRsntLg0kF1WT2H6rkzgv2XEL3+\nRiLvuoWxa7dyUHMJnJNJc9UNvciSQO/ICxizrvXYmE4Q0xLcMPR9elNHmPK7z4jOUAeKt4hk2CaX\n7+vh3o9dzm337OSad7rZBE0Bd59TY4ufJ8M2l12f6jgOxw+O89rz58l6/Y99Ro7RL/wlpfODAFi5\nHFYui7phA75Nbvpz7vVXmfi7v2Xov/wnsG1e7H0Has3iTjlAbHA9kn8rgUquapl5YT92Pk/s5lvw\nyRYlQ6aoXdjAO1EU2bzddWTJskgxbzAxuvSCXiZVrHsuNcLUeIav/vULPPHQ/MWbc6emmZ7MMTH6\n1lEwpUgEO5dbcZ/ri7iIxfDzW/n9NoJfknEcG8OGp8dnOTCTRRQF7uheuObprYhynapfzhAKuJM5\n2degDYhSVlQvTHsagKbmKjltVBtbrqmL+5qQJW8CZ2SBzrrXlYOUymRWEAQEUVlbmJJXZzS3nrei\nqK6SqFatv1ViVrIsfjTk2reG8yWuZWFLqCCIFUW8dnJeIdbrqKiCGyCVnz3EPr/KI2EHLNCKBoZh\ncX4wQ8DI0NLskq9yL1y9TFSP1RBV3cQ0bBRFonNjjBOHJjh/ZppS0aS7d+FgqUjMT7wlyOhQEsu0\nkeQ3d53PtGws26n0+1zVNjSPqDZYEFrpQsL2jU2cHc1wdizNrr75xLeMdM4jqmGVW67oZlt3jM2d\nVcX2nKeY9kV70SyDU8kzvDp5gGs6rmBSa0MUwe/bR7H0DLkFrL9z7bXrhfLik20VGRl7EdiDk/gJ\n09MJIm37iG98d93ra+8hQRAQJf+qiCq49t/V1KjatkNRMwn5ZTcUKT+EKIeItF5NeuIZSpkBgvFd\nrpoKBGOXuMcsCIhykFJxAhWbIdNhdOJ1omqEvz74t+yKXgZ0YS6iLOq62dD5YuVyjP/N/8S/eQub\nfu/fV36vpjdQDGcx1WkktYloxzuJOjcyeerL3rkXAIdS5izh1isX3O/+0Zf4wcBj1e2KChsjXbxv\ny11sjy+c8ShFI3Rmz9D+jn3svPeKqr3ZS/mVgyHObLTZNVAi9/prBLZsXXBbAMdnT+HzEsTFQICO\nX/00AL9ZSvKliT8F0pSSM+y6u4vmQ4+TeXY/iQc0uj7zuYpNtoxR0b3Wu2oUVd3SEQSBrk31z0l/\nQEXK6cw2SBSuhWGbyMtIXjcMi2cfO8Wpo25QUtxzQkjnjpI/8AaF48fo+sxnsbLu/ny9ffg3bQJB\nIPfaqwAobW3E330XA8d8+GtC9Po8RXVwfB0DlcZH8W3aRPKJxxFkmeA73oVv6BjJov+CK6oAV17X\ny9RElkuv6uaZx04xcCLRsMVZGbZt8w//y3U7tLSFiLfWh1aZhsUrzw1y+LVRLNNm4OR0JcTP0E1K\nRZOMt5iQni3C4pfpBYMUieCYJnaphBQILP2Gi7iIZeCiovo2gCrJgIHpCJxKuwrDcxNJ0vpbx/Kx\nHMhloqrmCAbKiur8wXxjX5yOjdELav2tteX5AosQVX/T4oqqV19Xa6UURHVNiioNUn8BJHlt1l+n\nJgimjKdGZ8l6ibiJ4tK1mI0UrvXsoVqL5k33kva76bBK1E31LWkWQ2dnMC2H9uwgarub3FiuJ9WK\nOrPTeWYSVWVO1yxMw7UsdnqW1IOvjAAL16eW0dXbhGnYzE4vHrayHtC9CfCaFFXNVX9rFdWjMyf4\nb69+gaHc/PYhi2HHRvdeXcr+m/EU1WhIRZbEOpIKcDblKlXbmjZzTec9+JUeHjj1IEOZEWzbnQir\nyg4kMdzA+ru2xZnF4NgmtplHlEMEmnZS8rntVjrb9wKgFyfmvceecw+Joq/iLtCLU4wc/lPyyaPL\n2r8siatK/S332g0FFCwjjWVk8YV68Me2A5BPHgaq9am1rTsEyY/sBZMNmxZfO/4t/ubQ3wFwMnsU\nJH1RRdXQrIaKqj4+Do6DNjyE47VVMxIJCs+eQPuHYZpCt9O167PISgRZjdG29WMogU6auu9wjzV9\natHPPFlwSd4v9N3Gvo6raAu2MpA+z1PDP1v0fVI0huRYdCrZuuRuLede093tWyhu6cSQBTKvvbyk\nMvTc6IuohgOKgiBXz0OzP05/r5vGnpuewHEcCkcOuT+/9iqFUycxEvVq2njB/XlDsL3STkZf4Lkh\nhYIEjQzJZGnRYzSXoahmUkUe/OobnDo6WXExJWcKxJoDKGn3mBzDYPT/++9MP/hdAKLX34joDxC/\n/U7CV11N97/8V/R9/o9petetaLpVCVEDaIn5CQcUBifWN1Apf+ggxuQEkX3XYwX8yKJDyZQq98OF\nxLZL2vnwr15D/6Ud+PwyA6cSi34vxw5U61jn9vJOzRb43ldf58BLwwSCCsGQ+xydnsyRmMjywJde\n4ZtfepnUjDs2ppMLt1K60JDC7sL1xUCli1hPXCSqbwPIooLjGBQslYJpE1NlDNvh8ZG3V++u2uTf\nYKCE7QgV0leLq9/Rx/t/+cp1a22yXHzsN/Zx78cuR26Qslquo6slqmYDomp6iqqsVifnoqisKfW3\nGqY0x/q7zEm7ZRaYOvNVsolX6n4/N0xpoqDx/GSKZp9Cs09hqqQvOVETGhHVyuR9fYmqIAjYnjLo\ni4JiaeiGw5nj7qR1Q+4cyY6tlHQT1UuA1EtGxV7VsdH9TnTNVVRlWaLDI6rjw+73296xOFH1e30q\n9QswGdK9Otq11KhWFFVfHNuxefjsY/z1wS8zmBni6clDtG6+n65dv72sbZXrTJcKVKq1/jbCmfQ5\nZEGiPdDJj4ZzRIJ3UjR1/uehryAIVWdDSO0iO8f6KzRI1l0vlBee/JGttG2+n4LSiQB0d12PrMYx\nSol575lrnxckf6X2Oz3+NLZZID97aFn7V2RxVdbf2sTfsu3XF+5BDXSihjZSTJ8kN3MQLT+ML9RT\nlwuQr1FLb+//CHtaLsF0LOK+JizHQmodaximBG5PT9O0GzpfNC/wxjEM9IlxtJFhzn/+P6KPjkDB\nItC8vW7hTQ1soPOSTxNtvw4l0EExcwbLWHgxaKowjSiI3N13O7+y68P83rW/Q9zXxEB6cNExS464\nY4CZqScHumfzjkZbuG3LrZzrUrES0+gjIwtuazAzxLnMEGFbQQrOLxfpat+KLQCHj3P6N34Vc3YW\ndaNrd0888A2MqTlENT9Bsz+OX/ZVa1StBYhq0CWqluVUEnrLODF7mqeG3EAq0zaRpYWdE47j8MMH\nDjE9lWPn3k7e//Gqit3TF3dVX1Fk4//1rxEDAcyZGQLbd1QWBNs+/FG6PvM5QnsuRfBqUku6hb+G\nqAqCQF9HhESqRK64tsX1iqI6MkzyiR8DEL/jTjTNqy03ZIqaOe8aGJrMVnoNv5mQJJG+7a3kszqT\nY42JuWlavPZ8NYu0tpf32RNTfOfvXmNmKs+uyzv56K9fy/W3unLpKz87x/e/9gbZjIZp2JSK7udJ\nzb459fqrQTX5961jR76Itz8uEtW3ARRRxnGqA/x7N7XRGfTxxkyWEc/aaNkO3x6Y4OtnxjmfXdnA\nNV2cxbBXNoivpgZB9jW7rQBCBQKBEqYZRFhhGuGbiVg8MM9iVUbV+hurUVTn267mWn/BDVRaU41q\ng/Y0UK1RNYwiRbNxXY7jOEyf+zal7DmSI4/WtX6oDVNyHIcfDCWwca+vjoBKybLJLbDdMqp1eVWi\nWlaZ1tv6CyAoLlkKRRwUS6Nkipw/O0NQTxNuDfOfHjzLE68M4wtUierZ41PIssiO3W7Kqa6bGIaF\nokjE4gH8wepErm0JolqxFF+AFjWaUSaqq1dUXXIl8HziBP/jjS/y2PmnaPE30+SLcWL2NGp0e0P7\nfSOEAwrdrSEGxjKLEqpUTgOhsTOhaJYYyY6xKdrDcMHEAXRbpLfpRrKGiSCIlJenVKmtgaL65hHV\nuYtMac0gokjIooASaMM2C/PI09zFHlHy4dgGWn6UYvokAFru/LJarrg1qisfV3PlHqoBBS3vEdVQ\nD4Ig0NzzHkBgdugHAASaLql7b6FMhASJ1vhOfvOyT/L7+/4Vv3PlZwCQIkmsBb7rcpumsqJqaxpT\n3/gHJr78JbIvPF95XeaF5xn5sz/BzuUIX3U10XfehNy8cNlKqPkywCafPLLgaxLFaVr9zUg1tdVb\nYr3kjDyJ4vSC75Oi7ndrZeon0mY+jylBPNTKtR1XMuEluU6/8vy8bYAbovSdUw+7x2uKiA1sjpua\nesj7veebtyDQ/O67iOy7Du38INrwEEpHB9F33kT81z9NVs/RFXJrhEVBRBakBRVVMRAg6F2vqdl6\nRe2hs4/y5Itv8NA3X8c0LRRh4fEjNVsknSyyeUcr77qrn3hLkFjc/Swb+5oxElMozS0EL9nJpt/9\nfwhdcSWtH7x/we2VyxX8c8asvs71CVSSI1HU7o3kjxymePIEwV278W3sQdfdOU/JlLFsB73GQj+b\nKfFHf/cqD/5sYE37Xi629rcBbthRI5w8PEEhp7P32h5Un8TEaAbLsnnuidM8/uAxHMfhtnt2cvMv\n9CPLEl097jNv+FwSSRIqz7Ey0sm3EFENe/fXRUX1ItYRbx2WcBELQhFlHC/tVBEFtkeD3N3jqpOP\nDE/jOA4n0nnemMlyJJnja2fG0Za5Mp/S0vyHF/6Y39//eaaLy1Noj82c5N8+9x85OnNyRZ9DFBUc\nwkQjOfw+A9Oen/j7VkVSSxNSgqheqA+A1SDww9RcQltraV5zjeoC1t+y7fapWZU/OTTYkKya2jRa\nrrp6q+WqiapVNUjlSDLHYLbIzqYQlzSFaPOI3tQS9t9GdXmm7p4DSZ6vMqwVkqeoRsIOiq1hOiKW\nadOeO8dEm7vynMrrqCH33CSyAqnZIr3bWioWKr1Utf4KglCx/8ZbgkvazRVvUn4heqlWrL+r7KPq\nOA5GaYqioPLtMz/iVOose1t387vX/Asub9tDydI4mxpc0Ta3b4yhGRbHzyf5g799madfn684pfM6\n4c1RHkjM8moizTfOjpPS3Ot/Ij+Fg0NvdCNZESHwAAAgAElEQVRnanrgKsouArJLDrZG3e9OkuKU\nrFLdIpogiO7CzxqJqmXk55FdSy8vMkWxHYe0YRLzQmEUvzv5rO1D6thmJayq7B4oE+nU6OPe+9px\nbB29UN/7sRFkabWKqmf99cto+WEEQUYNuLXzamAD4darAJcAl+tTywjaXm2m1z9VEAQ6QxuIqN7Y\nLFqYCyiqZVeB4imqs4/8kNSTT5B5/jmKp6vW3eRjj2Bls7R//BN0feZzdHziUxX1bS6SpRT+2C4Q\nRLJTL1ZSlcsYzY3zvTM/JGfkaQu21v1tS6wPgIH0wp3zpHAYBGHeRNouFtEUkbg/hizKXHLDXZgi\nzHhEtTQ1wcRTj1UWaF+ZeINzmfNc0X4ZQklDCswf69oCLUSK7vcpt7QQ/4W7CV99Na0fuB9Bca8r\ntX0DHZ/4FDPbXIWyO1zNPFAkFd1qPP6KwSBB73qtJSqWbTGen6BpupuxwQxCUUVepBZ93HNHlIPi\nAHZe3km8NUhnZxArnUZpc699tbOL7s/+NoFt2xfcXskbF31z7OB9XqDSmWWEDC2F+B13gvc9xO90\na8arRNW9FmvrVKeSRWzHYWSJet71wsa+OKpPYuDEfPuvbdu88eIwkixy+b4eNnTHSCeLPPvjUxx+\nbZR4a5APfuKqOjIajvrp7m2irSPMBz9xFZdds7Fum9l0qS7F/h8TFUU1d5GoXsT64SJRfRtAERVE\n0R0AdsSCqJLI1miQnU0hBrNFjqXyvJpwHwC74yHypsXPJhaPrS9jujiLg0POyPPwwI8bviZn5DmV\nPOO9foavHP06eaPAcHZhW9RCEJVmFMUdVC07tMSr3xpwHIekliLuKU+CqCL7mtFy5+dNdk0tiSAq\niHL1swmiAo616kb21dTfxmFKp4s+NMtmogGpLKu+vpBrOau1IdaqQSdTrlJ0uxfQ1e5ZZxOl5dWp\n1iqqWtadKNb2ZFwvKGoUw3GI+SzkmnO/ITfIS5Y72TMMG1/IJQwjeZf09LZC8pt/D7hJwY4Dsrfq\n39HtEtWl6lPd/bvvMfQ330ZWVVRXN0xbZg7bKjHtEY3P7f01Pn3prxBUAuxqcQnLieTpFW1zuxcQ\n8lffO8xIIsfXHp9fS5jMlAj1uRPT7w1OcXg2x3fOTWI7DnlPkYzIEc5mCqiiwO54iETJ4oqOewDY\nGg0hCeDgfh/5ufZf0VdxA6wGjuMwcfKLTJ35Wt1E0qpRVLOGhe1AkzfhVvzeteXZf21LY+rsN9wU\n3ei2ituhvHhUttlGO94JQCk7P0HWtjT04mRlEWu1RLXcnzLsdzCKk6ihrroU51jHTQiiDzXYhTyn\nTdER271Pmrturfu9IsqIiCCZC9ao1iqqxswMyccfQ47HCezor77II6TtH/tlrGv3ci59Hsu2yOpV\n0pDWsrw6eYAvHv57fv/5/5cHzz9NtP0GLCNNeuKndfv80uGv8qRna20PzCWqbgrtYosvgiQhhcPz\nrL+USmiqUBnj9/XdwHh3CH8iTXJkgKHf+10yX/8m06ePUDJLPHT2ERRR5r5N78YxzYaKqiiIGN54\n0fSRj+B73108OvIMVixE/N13AaC0tZPT8wx5z9JaoqqKysI1qsEggbKiOlNd8EkUpzFsE1/Rff6I\nuoK8SKJ3uQ9r16ZqaN4V+zbxkV+7FiEzWznG5aKcCD63rr5/UxM+ReLZg2MYCwSGWba9aBCS49jo\nhXFCV1+O3NyMb1Mvwd2XAmAaHlE1vF7XNdtJZt2xIpFavfL49BujnBtfnp1VkkV6t7WQzWhMT9aT\n4zPHpsimS+y8rINgSGXLDvcaPnFoglBY5QMfv5Lm1vnzons+spcP/bOraWoOEm8JUq6Kkr1Av2MH\nxhd0PtRi8PQ0rzw3uKzPsRpUrb9vLlG9mCr884WLqb9vA8iigui1a+iPVQexu3paOZnO88PzCTKG\nycaQj/s3d3A+O8hzE0n2tceIKIt/xbmaCcNkvnFU+j8c/w6Hpo/ya3s+zqODP6HgxftrC6z2LoZw\nvJNcwlX1BKVziVe/NVAwi+iWTtzvPswFQSAUv5T0xDMUUicIt+zF1JKYehpTn0X2NdfV19b2UhWk\n+T1Ll8RCiqocIOcESFvu9qdLOpsj9RMmy+sLGWzahWXmyc8eIBDdRjC+qy4IZjiv4RNFNnhKarv3\n70RheUS1rEaBa3VEEFFDGxd51+rgk31M2zZNqongEfiQkSYSVTlR9IHg1nb6w+55cBCQRRv7q3+B\nLUeh5ypyaU9F8gjg5h0tHHxlmK2XtC25/wtp/dWNxpO+5cIouvfziFagO9zJzpZqiE5H0J18li3t\ny8Xera20xvxMe4mTPe3zXRHjswXkdh9CxFONRIGBbJGXptJg5VGVPRzLbmBaM9gdD3NTR5yjyTzH\n0u7njasKrX6VRNH9DrN6niZfdSItSr41taexrSKWkcEyMmi5c/gjbkpsxQmgRCtBdU2+MlF1r41i\n5gzBpl0kzn4DvThOINZPS98HKtuuvb9jHTejBFxlpJQdJOaR1jISAw+g5QaR5DBdu3/bDVMyVz4B\ny5fK/V5nQa8uSpUhKWE6L/n1yjhUizc0gwOOyB+Fuut+LwgCqqRiSuaCE+Cyoqr6JKa/+20cw6D1\nAx9CkBWKp1y3TfvHfhkxECB0zbX855f+jJlSkv7mbRybOclnLvskD559hPF8fajX/tEXueuGf4s0\ne4jc9GvEOm4mbRTJ6Fmmamy9cxXV7nAnQTnA8dlTOI6zYMaBFIlipqrXveM4SCUDLS4R97tEVRFl\nIldeDcPPcOr7X6VM76eHz/C8NEJaz3J33+004WMWGhJVgJGP3sLowRfYtzHC8NjLPHLuCQzL4H13\nvQccB9+11/D7z3++4hqoI6qSgr5AjaoYCBLSU8iiw/FD42zb1U5Hd4yR3DiiJaEY7vHIuq8SzDQX\njuMwPpQiEFLqUu/LKKcSlxXV5aDkjVm1NaoAIb/CzZd38fgrw7x4dIJ37u2a997vPjPAMwfG+JPP\nXF9ptVSGbRaZOPVlTG2GUPNeev/wP4EoVr5j0yzhAzTTvV+Pn0/S0RxEEASSOZeozmY1TMtGlla2\n8DeVKvLVH7vX85d/99YlXu1ia38bp49O8er+QbKpEtt2tXPFdZt4/cUhRFHg8n3uIu7OvZ2cPzvD\n4OkZ9t28ZUFHT+21LCsSseYgqZkCW/rbOHV0kv0/OcPpo5Pc/r5dFev2XEyMpvnxg0exLYdIzM+R\n10ZRVIk779u1YEu2leLNJKr65AS5119Dad/AxBf/F1IkitLWhtK+gdBHP+QGV1zEP0lcVFTfBlBE\nmULpZ/iENHtbqqpPq1/l2rYoacNEEFw1TJVEbutuRrcdnhqdRbdsMouoP9maOsvpUmMV9tC0m1r5\n5aP/wGhunG1Nbi83bRX2u9qQof4rb17x+y80LNvinGcli9fU8gWb3ZXcQtJVKGeHf8TUmb/HsY15\nPSvFGqK6GpTb0yDOD1OacKqTiEShfsW4ZFn8cMLhdXsX30k0MdJ0H4KoMnP+QfTCeMU+qaMwXdLp\nDvkQvQfihoCKJMBYYWlCIEo+HMd0U1OtEnpxAl+we137qJahSipJy8EnOkiKez7bMwMku7ZTXmbW\nDRtfpLqg05oawBeLEepy678K3gp7uQ1SLB7kE5+7gb5t9RPfRqgoqhegV1/Z+rvaGtWyTXXSNOgJ\n1xORsrWzVtlaDoJ+mc++/1LC5VCpOepISTeZzWjInh30ipYIv7VrEwFJ5LGRaY6lBQL+60loCn1h\nP/f2ttET9tMT8lN2mMZUmXa/io2EIIT44uH/w3dPP8yJxBkcx3GvtzVYfy29SlIyky8ArlpTTJ9C\nkPwo/lZSHgmrWH8D7ajBbkqZM4we+Qv04jih5stp3Xx/3XVeTv/1hXrwRTYjKSGUwAb0/HCl1hxA\nK4yh5Qbd4/GUb8Xro7pStaASpiROVfY9F7IvXhei5H5mh4yWIaw2bkHlk3wgWQsqquXFGiGXIvvy\ni/j6NhPZdz3hK6+i6fY72fAv/iWRm24muu969o+9xFRxGsuxOOaVjHzr1EOM5yfZ3rSFe7fexb+5\n+p/zgW3vxXQsXhh/g3DLFTi2QW72CP/jwBf5k1f/R93+W/z16rAkSuxs3kFSSzGWn5/QXHldNIpd\nyLttNHSd1JM/QbQddJ9EWKmOG5fddC8A8YNVNXxm5AxPDT1L3NfEHb3vonjSTVKWQo3dQZt2Xctr\nu0KcTg8wVXBJ9jMj+8kLBq3v/yDpqFxnbW8LVGt3lUUUVTEQRHZM3rExh2nYvPKzQcC1RqulKumU\nDd+Cqb+z03nyOZ3uTfGGpL4c9rQiourNNXzq/DHrlivcMej4+cbzjCMDsxQ1k6EGday52UOYmlua\npBcnkUKhuvYnlrd4HvOI0tceP8Vzh91k3aQXNuU4MJNZ+QJXchXv2bS1hbaOMIOnZ5hJ5Hn9hSHO\nnZomOV1g+652IjFXeBAEgTvv282H/tlV9F/aseztl0P/9t28mV/81NXs2L2BqfEs3/7Kq5UWQ7XI\n5zR+/H2XpAK8/MwAiYksY0Opde0L/mYS1cQ3v870d7/N+P/+a3eMFKB4+hSZ555l5FvfWff9XcRb\nBxeJ6tsAiihjGCdokQ+gzKntkaxjaPpxtgWH2eGprVe3xmj1K7ySSPPnh8/zl0fOYyzQC68cViKL\nMkWzSMGoD2bQLQPRCzyyHZveaA8f7f8gAJq5sNqW1rKM5sbn/T4Q60dW47Rt+SiS9NYV9IeyI/zp\nq1/gXz377/mfh74CzJlE+JpRQxspZc9h6pm61hVze1aWQ4XsVSb/LmT9FUSFUaoPt0Sx/rt7ZjzJ\nwZyPl+29nC6IPDJeItLzfhzHJDHwAIb34B/XRBxgo1fXCSCLIh0BH+MFfcEatcpxeHZHyywwO/wY\n4OAL963qsy4FVVSZ9hSeSDBJtJSgK3uaA1JnJYTHMC18Ncryls1R+v7oPxPZ7FoDC14qrbwKAlhe\n8b4QNapaRVFd3TBtFF1FJGHZbIzUKxiqpOCXfHULVctFb0eEv/ztd9DZEpyX4jnpJVAKskC7X+X+\nLR20B1Te19uOYTuczbkLVXd3S/zaJRsJe46PGzuqi0BRVabbs25vb76OvFHgqeGf8QdP/RnPjb2E\nIJYXRlb3HZg1teWl7Fn04iSl7DksI0uoaTeCKJPSyyqle3yCINK+7ZcIxvegBruIdd5K86Z75oXB\nqcEOBEEm1nVrZfLvD/fhOCbF7NlKvXl2yu2hKMkueXQcs2LjWyhl98xImtEGbZHKipHquGNQI6La\nCAWziOlYRH2NswL8kg9BNBe0I5cVVe3g6wC0f/ijCKKIIEm0/OKH+cOZb/NXB/+WklnikXM/QZVU\nBKqEaKbkTpB/47JPcGfvLfRGe7ih6xpEQeTwzHFCLW57l4mJ5yrtaABu6LyG+7beza6WGouxhx1e\n653vH3hxwc8te4FKxdOnGPr8H5H45j8A4EOqI2z+WDOZLe74mgp7dbgjA5iOxT09t5H54Y8Y/9L/\nRlBVYu9svOi6OdaLKIicTg0w5X0G3TZ4YuinACS1ekdDbTiUKqkYC7iWyinDnWIS1SeT964Bl6hW\nSbNi+JAXIKpDZ93z37u1cU9kY7pMVFdu/Z0bpgRumxrwwtbmQDcsxrxre+417jgO+ZnXQRARpUDD\nbIiyw2L7pg38h09egyqLfP/ZATTDqtwfsDr7b9k9shJIksgd9+6qI6QHXnKDzq64btO81y4V4jcX\n19+yhfd9dC/hqJ+W9jC33bOTW9/rlnM8+fBxXts/WHmtZdk8/v2jFHI6G7rdaz+fq15Xc+3Ja4Ec\ndRe9ah0L6wFjOkH+iNtmC9um6eZb2PInf862v/7fSJEoqUOHcRwHq/jWCZa6iPXDRaL6NkD5QWPO\nSeZNa1meHH6SkvYcBWOw8ntJFLizuxUbyBgmRcvmyGyOl6fS81bry+0f+qLu5Ga66D68fjq8n8cG\nn2Q0N4bt2HSHO9nTcgmf3vNxQor7kFxIUbUdm786+CX+6yt/yXC2PkRE8TXTtfufE4gtHMjwVsAb\nU4c5lxmiJdDCdZ1X8+Ed93Fj93V1rwnFLwMgm3gZ26wJhpmjqAprVVQdCxDnrXqPFTRO2n0EKaCi\nM11Tl5PWDZ6fTCFhc4P4OtvCMrrtcEhrJ9b5Liwjg54fQVbjjHmBH7VEFaA75MdyHCaLi6tX5bq8\nqbNfo5A8hBrsItK+b1WfdSn4JJWEN3EONGlcM/IjggGZl7IBtnbHEATQTLtCKH2qwJ5PfQjRH0AN\nuuSn4IXPlInBSqBUrL8XoD3NGlN/9eIENgJJ26En0j3v7xE1TEZf3cq3IAhEAgr5ooFdQ6zGZvIg\ngCUKBGuO+7LmMHviLiEyzGG2N0Uq6j3A7niYmCojCRBRZHZ5r40HL+Xz7/gD7uj7KH7fdZzLzFST\nf1dZp1omqqFmlwhlp16o1G4HmvcAkCpbf2tCYUTJ//+z995hct3l2f/ntOllZ3vflVYrrbpsyZa7\njY3B2ICNDQQIhBZISAIhIYSUNxAgyS+dBAIheRMg9BgcDCbg3pssy7Zk9bK9t+n9lN8f55wpOzMr\n7Up2Il7d1+Xrkmdm58ycOeV7P/f93A+NvbfTuuGDBFuvqqpCeeo20rntU7h8PYXHXH7TgTI/+J8s\njv4UNR8nFTmE4moqzDo1dK1gSazWw6frBn9350t8+b9ernhuNpxGFAyE/DSKq6mQBn46RLPWSBZH\ndcucS3aCpBYKJkthF2uM2Ul8uy7B3V+0lsdycfJ6nmPhk9w7/DDxfILXdl/LQH35db/RVY+75PO6\nZTft3lbG4uMIsg/F14tLjdKmFK9N25u2cGPPdYUCqg3DMNj3vPnvwdhw4fGR6Tj7jhXbWuzk3/Ev\n/C25iXHm68zjNNFUSRZcv/IOvvGmer75xno0AQLxPD2TWRq/8gMW7/kxciBAx8c/gWvN2pr7sMff\nxWh8nLH4BA2uEHXOII+PP0M0G2fRst6vD60rJC3bcIgKqqGhV8k2sOdVqvEEbo9C1rqmTSSmCOaL\nBVU556pJVIdPLiBgEIqNVn1+NdbfQo9qFUVVlkR8bqUwvqoU43NJdGttMrmEqOaS4+Qzc3iCG3F4\n2tC1dEXIlmEn2MtOulv83HhJF5FEjgf2jhGOF4nmfGTlpHO1va3BkIdf/vXd9K5rIJdVmZmM0d4V\nJFSlB7UalutX9/icdPSEyl6zYUsrb3v/TvwBJ889Mcz4sLmWe/qhk0xPxFi3qZkrb1hXeH3AsgjP\nnWUScylEpxM5FCI3UylSnA2ijz8GhkHo9TcRuOoaGt58m7k9xYFn40by4TDxPc8w9KnfO6fbvYD/\nHbhAVM8DCIKALMoVI2R+OnhfoU+0tOIMZqjS2hJV6QdDM9w9MstkqnxxZ/eo9thENbPIT489xA9O\n/Jh7Bu9j/5xp+72h6xo+sv0DhFx1piWM2j2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KT6ufz//qbpyKxN1PDBVSl1s7i+f0lp2d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fjiRZ9SxbyqxF+rvLhF0AbeHNBVBct2MK+wxUw1I2G97L+VssmUG0a4Yf29fvFG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\nqmKjKkmSJEmqio2qJEmSJKkq/w9laTfnvYbz2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112d706d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from abupy import ABuScalerUtil\n",
    "# ABuScalerUtil.scaler_std将所有close的切面数据做(group - group.mean()) / group.std()标示化，为了可视化在同一范围\n",
    "p_data_it_close = ABuScalerUtil.scaler_std(p_data_it_close)\n",
    "p_data_it_close.plot()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.ylabel('Price')\n",
    "plt.xlabel('Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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